Anaissi, A, Kennedy, PJ & Goyal, ML 2011, 'Dimension Reduction of Microarray Data Based on Local Principal Component', World Academy of Science, Engineering and Technology, vol. 77, pp. 68-73.
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Analysis and visualization of microarraydata is veryassistantfor biologists and clinicians in the field of diagnosis and treatment of patients. It allows Clinicians to better understand the structure of microarray and facilitates understanding gene expression in cells. However, microarray dataset is a complex data set and has thousands of features and a very small number of observations. This very high dimensional data set often contains some noise, non-useful information and a small number of relevant features for disease or genotype. This paper proposes a non-linear dimensionality reduction algorithm Local Principal Component (LPC) which aims to maps high dimensional data to a lower dimensional space. The reduced data represents the most important variables underlying the original data. Experimental results and comparisons are presented to show the quality of the proposed algorithm. Moreover, experiments also show how this algorithm reduces high dimensional data whilst preserving the neighbourhoods of the points in the low dimensional space as in the high dimensional space.
Beck, D, Ayers, S, Wen, J, Brandl, MB, Pham, TD, Webb, P, Chang, C-C & Zhou, X 2011, 'Integrative analysis of next generation sequencing for small non-coding RNAs and transcriptional regulation in Myelodysplastic Syndromes', BMC Medical Genomics, vol. 4, no. 1, pp. 1-16.
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Abstract Background Myelodysplastic Syndromes (MDSS) are pre-leukemic disorders with increasing incident rates worldwide, but very limited treatment options. Little is known about small regulatory RNAs and how they contribute to pathogenesis, progression and transcriptome changes in MDS. Methods Patients' primary marrow cells were screened for short RNAs (RNA-seq) using next generation sequencing. Exon arrays from the same cells were used to profile gene expression and additional measures on 98 patients obtained. Integrative bioinformatics algorithms were proposed, and pathway and ontology analysis performed. Results In low-grade MDS, observations implied extensive post-transcriptional regulation via microRNAs (miRNA) and the recently discovered Piwi interacting RNAs (piRNA). Large expression differences were found for MDS-associated and novel miRNAs, including 48 sequences matching to miRNA star (miRNA*) motifs. The detected species were predicted to regulate disease stage specific molecular functions and pathways, including apoptosis and response to DNA damage. In high-grade MDS, results suggested extensive post-translation editing via transfer RNAs (tRNAs), providing a potential link for reduced apoptosis, a hallmark for this disease stage. Bioinformatics analysis confirmed important regulatory roles for MDS linked miRNAs and TFs, and strengthened the biological significance of miRNA*. The 'RNA polymerase II promoters' were identified as the tightest controlled biological function. We suggest their control by a miRNA dominated feedback loop, which might be linked to the dramatically different miRNA amounts seen between low and high-gra...
Boyle, JR, Sparks, RS, Keijzers, GB, Crilly, JL, Lind, JF & Ryan, LM 2011, 'Prediction and surveillance of influenza epidemics', MEDICAL JOURNAL OF AUSTRALIA, vol. 194, no. 4, pp. S28-S33.
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Objective: To describe the use of surveillance and forecasting models to predict and track epidemics (and, potentially, pandemics) of influenza. Methods: We collected 5 years of historical data (2005-2009) on emergency department presentations and hospital admissions for influenza-like illnesses (International Classification of Diseases [ICD-10-AM] coding) from the Emergency Department Information System (EDIS) database of 27 Queensland public hospitals. The historical data were used to generate prediction and surveillance models, which were assessed across the 2009 southern hemisphere influenza season (June-September) for their potential usefulness in informing response policy. Three models are described: (i) surveillance monitoring of influenza presentations using adaptive cumulative sum (CUSUM) plan analysis to signal unusual activity; (ii) generating forecasts of expected numbers of presentations for influenza, based on historical data; and (iii) using Google search data as outbreak notification among a population. Results: All hospitals, apart from one, had more than the expected number of presentations for influenza starting in late 2008 and continuing into 2009. (i) The CUSUM plan signalled an unusual outbreak in December 2008, which continued in early 2009 before the winter influenza season commenced. (ii) Predictions based on historical data alone underestimated the actual influenza presentations, with 2009 differing significantly from previous years, but represent a baseline for normal ED influenza presentations. (iii) The correlation coefficients between internet search data for Queensland and statewide ED influenza presentations indicated an increase in correlation since 2006 when weekly influenza search data became available. Conclusion: This analysis highlights the value of health departments performing surveillance monitoring to forewarn of disease outbreaks. The best system among the three assessed was a combination of routine forecastin...
Budka, M & Gabrys, B 2011, 'Electrostatic field framework for supervised and semi-supervised learning from incomplete data', Natural Computing, vol. 10, no. 2, pp. 921-945.
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Budka, M, Gabrys, B & Musial, K 2011, 'On Accuracy of PDF Divergence Estimators and Their Applicability to Representative Data Sampling', Entropy, vol. 13, no. 7, pp. 1229-1266.
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Choi, JH, Ryan, LM, Cramer, DW, Hornstein, MD & Missmer, SA 2011, 'Effects of Caffeine Consumption by Women and Men on the Outcome of In Vitro Fertilization', Journal of Caffeine Research, vol. 1, no. 1, pp. 29-34.
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OBJECTIVE: The objective of this study was to estimate the association between caffeine consumption and in vitro fertilization (IVF) outcomes. METHODS: A total of 2474 couples were prospectively enrolled prior to undergoing their first cycle of IVF, contributing a total of 4716 IVF cycles. Discrete survival analysis adjusting for observed confounders was applied to quantify the relation between caffeine consumption and livebirth. Secondary outcomes of interest were oocyte retrieval, peak estradiol level, implantation rate, and fertilization rate. RESULTS: Overall, caffeine consumption by women was not significantly associated with livebirth (ptrend=0.74). Compared with women who do not drink caffeine, the likelihood of livebirth was not significantly different for women who drank low (>0-800 mg/week; odds ratio [OR]=1.00, 95% confidence interval [CI])=0.83-1.21), moderate (>800-1400 mg/week; OR=0.89, 95% CI=0.71-1.12), or high levels of caffeine (>1400 mg/week; OR=1.07, 95% CI=0.85-1.34). Greater caffeine intake by women was associated with a significantly lower peak estradiol level (ptrend=0.03), but was not associated with the number of oocytes retrieved (ptrend=0.75), fertilization rate (ptrend=0.10), or implantation rate (ptrend=0.23). There was no significant association between caffeine intake by men and livebirth (ptrend=0.27), fertilization (ptrend=0.72), or implantation (ptrend=0.24). The individual effects of consumption of coffee, tea, or soda by women or men were not related to livebirth. CONCLUSION: Caffeine consumption by women or men was not associated with IVF outcomes.
DIEKERT, V & NOWOTKA, D 2011, 'PREFACE', International Journal of Foundations of Computer Science, vol. 22, no. 02, pp. 275-276.
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Dong, H & Hussain, FK 2011, 'Semantic service matchmaking for Digital Health Ecosystems', KNOWLEDGE-BASED SYSTEMS, vol. 24, no. 6, pp. 761-774.
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The vision of Digital Ecosystems was initiated by the European Commission, with the purpose of constructing an information and communication technology environment to facilitate the sustainable development of small and medium enterprises. As a key sub-domain of Digital Ecosystems, Digital Health Ecosystems provide crucial services to maintain the health of the main participants of Digital Ecosystems. We are concerned with the large-scale, ambiguous, heterogeneous, and untrustworthy nature of health service information in Digital Health Ecosystems. An intensive survey found that current research cannot support accurate and trustworthy matchmaking between health service requests and health service advertisements in Digital Health Ecosystems. Therefore, in this paper, we propose a framework for a semantic service matchmaker that takes into account the ambiguous, heterogeneous nature of service information in Digital Health Ecosystems. This framework is designed to make four major contributions, which are health service domain knowledge modeling, online health service information disambiguation, health service query disambiguation and health service quality evaluation and ranking. In order to thoroughly evaluate this framework, we implement a prototype - a Semantic Health Service Search Engine, and conduct a series of experiments on the prototype using a functional testing and simulation approach
Dong, H, Hussain, FK & Chang, E 2011, 'A framework for discovering and classifying ubiquitous services in digital health ecosystems', JOURNAL OF COMPUTER AND SYSTEM SCIENCES, vol. 77, no. 4, pp. 687-704.
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A digital ecosystem is a widespread type of ubiquitous computing environment comprised of ubiquitous, geographically dispersed, and heterogeneous species, technologies and services. As a subdomain of the digital ecosystems, digital health ecosystems are crucial for the stability and sustainable development of the digital ecosystems. However, since the service information in the digital health ecosystems exhibits the same features as those in the digital ecosystems, it is difficult for a service consumer to precisely and quickly retrieve a service provider for a given health service request. Consequently, it is a matter of urgency that a technology is developed to discover and classify the health service information obtained from the digital health ecosystems. A survey of state-of-the-art semantic service discovery technologies reveals that no significant research effort has been made in this area. Hence, in this paper, we present a framework for discovering and classifying the vast amount of service information present in the digital health ecosystems. The framework incorporates the technology of semantic focused crawler and social classification. A series of experiments are conducted in order to respectively evaluate the framework and the employed mathematical model.
Dong, H, Hussain, FK & Chang, E 2011, 'A service concept recommendation system for enhancing the dependability of semantic service matchmakers in the service ecosystem environment', JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, vol. 34, no. 2, pp. 619-631.
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A Service Ecosystem is a biological view of the business and software environment, which is comprised of a Service Use Ecosystem and a Service Supply Ecosystem. Service matchmakers play an important role in ensuring the connectivity between the two ecosystems. Current matchmakers attempt to employ ontologies to disambiguate service consumers' service queries by semantically classifying service entities and providing a series of human computer interactions to service consumers. However, the lack of relevant service domain knowledge and the wrong service queries could prevent the semantic service matchmakers from seeking the service concepts that can be used to correctly represent service requests. To resolve this issue, in this paper, we propose the framework of a service concept recommendation system, which is built upon a semantic similarity model. This system can be employed to seek the concepts used to correctly represent service consumers' requests, when a semantic service matchmaker finds that the service concepts that are eventually retrieved cannot match the service requests. Whilst many similar semantic similarity models have been developed to date, most of them focus on distance-based measures for the semantic network environment and ignore content-based measures for the ontology environment. For the ontology environment in which concepts are defined with sufficient datatype properties, object properties, and restrictions etc., the content of concepts should be regarded as an important factor in concept similarity measures. Hence, we present a novel semantic similarity model for the service ontology environment. The technical details and evaluation details of the framework are discussed in this paper
Du, C, Yang, J, Wu, Q & He, X 2011, 'Locating facial landmarks by support vector machine-based active shape model', International Journal of Intelligent Systems Technologies and Applications, vol. 10, no. 2, pp. 151-151.
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Active shape model (ASM) plays an important role in face research such as face recognition, pose estimation and gaze estimation. A crucial step of the common ASM is finding a new position for each facial landmark at each iteration. Mahalanobis distance minimisation is used for this finding, provided there are enough training data such that the grey-level profiles for each landmark following a multivariate Gaussian distribution. However, this condition could not be satisfied in most cases. In this paper, a novel method support vector machine-based active shape model (SVMBASM) is proposed for this task. It approaches the finding task as a small sample size classification problem. Moreover, considering the poor classification performance caused by the imbalanced dataset which contains more negative instances (incorrect candidates for new position) than positive instances (correct candidates for new position), a multi-class classification framework is further proposed. Performance evaluation on Shanghai Jiao Tong University face database shows that the proposed SVMBASM outperforms the original ASM in terms of the average error and average frequency of convergence. © 2011 Inderscience Enterprises Ltd.
Gil-Aluja, J, Gil-Lafuente, AM & Merigó, JM 2011, 'Using homogeneous groupings in portfolio management', Expert Systems with Applications, vol. 38, no. 9, pp. 10950-10958.
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Graham, PL, Ryan, LM & Luszcz, MA 2011, 'Joint modelling of survival and cognitive decline in the Australian Longitudinal Study of Ageing', JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, vol. 60, no. 2, pp. 221-238.
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The paper describes the use of a longitudinal tobit model to characterize cognitive decline over a 13-year period in a cohort of 2087 elderly Australians. Use of a tobit formulation allows for the so-called 'ceiling effect' wherein many subjects achieve perfect test scores. A Bayesian hierarchical joint model is presented that allows for random subject-specific intercepts and slopes, as well as for informative dropout. Results suggest several potential areas of intervention. For example, there is a clear dose-response effect of exercise whereby increasing levels of exercise are associated with higher cognitive scores. © 2010 Royal Statistical Society.
Gumley, W & Stoianoff, N 2011, 'Carbon Pricing Options for a Post-Kyoto Response to Climate Change in Australia', Federal Law Review, vol. 39, no. 1, pp. 131-159.
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Gumley, WS & Stoianoff, NP 2011, 'Carbon Pricing Options for a Post-Kyoto Response to Climate Change in Australia', Federal Law Review, vol. 39, no. 1, pp. 131-159.
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Australia's commitments under the Kyoto Protocol - proposed introduction of carbon pricing in Australia - policy options for establishing a carbon price - relative merits of carbon taxes and emissions trading - barriers to change within the Australian taxation system - argument that tax expenditure reform should be a key element of all market based responses to climate change.
Hart, JE, Garshick, E, Dockery, DW, Smith, TJ, Ryan, L & Laden, F 2011, 'Long-Term Ambient Multipollutant Exposures and Mortality', AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, vol. 183, no. 1, pp. 73-78.
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Rationale: Population-based studies have demonstrated associations between ambient air pollution exposures and mortality, but few have been able to adjust for occupational exposures. Additionally, two studies have observed higher risks in individuals with occupational dust, gas, or fume exposure. Objectives: We examined the association of ambient residential exposure to particulate matter less than 10 μm in diameter (PM10), particulate matter less than 2.5 μm in diameter (PM2.5), NO2, SO2, and mortality in 53,814 men in the U.S. trucking industry. Methods: Exposures for PM10, NO2, and SO2 at each residential address were assigned using models combining spatial smoothing and geographic covariates. PM2.5 exposures in 2000 were assigned from the nearest available monitor. Single and multipollutant Cox proportional hazard models were used to examine the association of an interquartile range (IQR) change (6 μg/m3 for PM10, 4 μg/m3 for PM 2.5, 4ppb for SO2, and 8ppb for NO2) and the risk of all-cause and cause-specific mortality. Measurements and Main Results: An IQR change in ambient residential exposures to PM10 was associated with a 4.3% (95% confidence interval [CI], 1.1-7.7%) increased risk of all-cause mortality. The increase for an IQR change in SO2 was 6.9% (95% CI, 2.3-11.6%), for NO2 was8.2%(95%CI, 4.5-12.1%), and for PM2.5 was 3.9% (95% CI, 1.0-6.9%). Elevated associations with cause-specific mortality (lung cancer, cardiovascular and respiratory disease) were observed for PM2.5, SO2, and NO2, but not PM10. None of the pollutants were confounded by occupational exposures. In multipollutant models, overall, the associations were attenuated, most strongly for PM10. In sensitivity analyses excluding long-haul drivers, who spend days away from home, larger hazard ratios were observed. Conclusions: In this population of men, residential ambient air pollution exposures were associated with mortality.
Kadlec, P & Gabrys, B 2011, 'Local learning‐based adaptive soft sensor for catalyst activation prediction', AIChE Journal, vol. 57, no. 5, pp. 1288-1301.
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AbstractThis work presents an algorithm for the development of adaptive soft sensors. The method is based on the local learning framework, where locally valid models are built and maintained. In this framework, it is possible to model nonlinear relationship between the input and output data by the means of a combination of linear models. The method provides the possibility to perform adaptation at two levels: (i) recursive adaptation of the local models and (ii) the adaptation of the combination weights. The dataset used for evaluation of the algorithm describes a polymerization reactor where the target value is a simulated catalyst activity in the reactor. This dataset is also used to evaluate the performance of the proposed algorithm. The results show that the traditional recursive partial least squares algorithm struggles to deliver accurate predictions. In contrast to this, by exploiting the two‐level adaptation scheme, the proposed algorithm delivers more accurate results. © 2010 American Institute of Chemical Engineers AIChE J, 57, 2011
Kadlec, P, Grbić, R & Gabrys, B 2011, 'Review of adaptation mechanisms for data-driven soft sensors', Computers & Chemical Engineering, vol. 35, no. 1, pp. 1-24.
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Li, Z, Wong, L & Li, J 2011, 'DBAC: A simple prediction method for protein binding hot spots based on burial levels and deeply buried atomic contacts', BMC SYSTEMS BIOLOGY, vol. 5, no. S1, pp. 1-11.
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Background: A protein binding hot spot is a cluster of residues in the interface that are energetically important for the binding of the protein with its interaction partner. Identifying protein binding hot spots can give useful information to protein en
Liu, Q, Hoi, SCH, Su, CTT, Li, Z, Kwoh, C-K, Wong, L & Li, J 2011, 'Structural analysis of the hot spots in the binding between H1N1 HA and the 2D1 antibody: do mutations of H1N1 from 1918 to 2009 affect much on this binding?', BIOINFORMATICS, vol. 27, no. 18, pp. 2529-2536.
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Motivation: Worldwide and substantial mortality caused by the 2009 H1N1 influenza A has stimulated a new surge of research on H1N1 viruses. An epitope conservation has been learned in the HA1 protein that allows antibodies to cross-neutralize both 1918 a
Lo, D, Jinyan Li, Iimsoon wong & Siau-Cheng Khoo 2011, 'Mining Iterative Generators and Representative Rules for Software Specification Discovery', IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 2, pp. 282-296.
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Billions of dollars are spent annually on software-related cost. It is estimated that up to 45 percent of software cost is due to the difficulty in understanding existing systems when performing maintenance tasks (i.e., adding features, removing bugs, etc.). One of the root causes is that software products often come with poor, incomplete, or even without any documented specifications. In an effort to improve program understanding, Lo et al. have proposed iterative pattern mining which outputs patterns that are repeated frequently within a program trace, or across multiple traces, or both. Frequent iterative patterns reflect frequent program behaviors that likely correspond to software specifications. To reduce the number of patterns and improve the efficiency of the algorithm, Lo et al. have also introduced mining closed iterative patterns, i.e., maximal patterns without any superpattern having the same support. In this paper, to technically deepen research on iterative pattern mining, we introduce mining iterative generators, i.e., minimal patterns without any subpattern having the same support. Iterative generators can be paired with closed patterns to produce a set of rules expressing forward, backward, and in-between temporal constraints among events in one general representation. We refer to these rules as representative rules. A comprehensive performance study shows the efficiency of our approach. A case study on traces of an industrial system shows how iterative generators and closed iterative patterns can be merged to form useful rules shedding light on software design. © 2006 IEEE.
Longbing Cao, Huaifeng Zhang, Yanchang Zhao, Dan Luo & Chengqi Zhang 2011, 'Combined Mining: Discovering Informative Knowledge in Complex Data', IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 41, no. 3, pp. 699-712.
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Enterprise data mining applications often involve complex data such as multiple large heterogeneous data sources, user preferences, and business impact. In such situations, a single method or one-step mining is often limited in discovering informative knowledge. It would also be very time and space consuming, if not impossible, to join relevant large data sources for mining patterns consisting of multiple aspects of information. It is crucial to develop effective approaches for mining patterns combining necessary information from multiple relevant business lines, catering for real business settings and decision-making actions rather than just providing a single line of patterns. The recent years have seen increasing efforts on mining more informative patterns, e.g., integrating frequent pattern mining with classifications to generate frequent pattern-based classifiers. Rather than presenting a specific algorithm, this paper builds on our existing works and proposes combined mining as a general approach to mining for informative patterns combining components from either multiple data sets or multiple features or by multiple methods on demand. We summarize general frameworks, paradigms, and basic processes for multifeature combined mining, multisource combined mining, and multimethod combined mining. Novel types of combined patterns, such as incremental cluster patterns, can result from such frameworks, which cannot be directly produced by the existing methods. A set of real-world case studies has been conducted to test the frameworks, with some of them briefed in this paper. They identify combined patterns for informing government debt prevention and improving government service objectives, which show the flexibility and instantiation capability of combined mining in discovering informative knowledge in complex data. © 2010 IEEE.
Merigo, JM 2011, 'The uncertain probabilistic weighted average and its application in the theory of expertons', AFRICAN JOURNAL OF BUSINESS MANAGEMENT, vol. 5, no. 15, pp. 6092-6102.
Merigó, JM 2011, 'A unified model between the weighted average and the induced OWA operator', Expert Systems with Applications, vol. 38, no. 9, pp. 11560-11572.
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Merigó, JM 2011, 'Fuzzy multi-person decision making with fuzzy probabilistic aggregation operators', International Journal of Fuzzy Systems, vol. 13, no. 3, pp. 163-174.
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We present a fuzzy multi-person decision making model with fuzzy probabilistic information. For doing so, we present the fuzzy probabilistic ordered weighted averaging (FPOWA) operator. It is an aggregation operator that unifies the fuzzy probabilistic aggregation and the fuzzy OWA (FOWA) operator in the same formulation considering the degree of importance that each concept has in the analysis. We study its applicability and we see that it is very broad because all the previous studies that use the probability or the OWA operator can be revised and extended with this new approach. We focus on a multi-person decision making problem that unifies risk and uncertain environments in the same formulation. We implement this approach in a political management problem regarding the selection of fiscal policies. © 2011 TFSA.
Merigo, JM & Casanovas, M 2011, 'Generalized aggregation operators in decision making with Dempster-Shafer belief structure', Information, vol. 14, no. 8, pp. 2711-2732.
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We present a general method for decision making with Dempster-Shafer belief structure based on generalized aggregation operators. The main advantage of this approach is that it gives a more complete formulation of the D-S framework because it is able to provide a wide range of aggregation operators by using generalized means, quasi-arithmetic means and ordered weighted averaging (OWA) operators. Thus, we are able to formulate the D-S approach by using the usual arithmetic means but also using other types of means such as geometric or quadratic means. We study different properties and particular cases based on the generalized OWA operator. We further generalize this approach by using the Quasi-OWA operator. Moreover, we extend this approach by using induced aggregation operators and the hybrid average. The paper ends with an illustrative example of the new approach in a decision making problem regarding the selection of strategies. © 2011 International Information Institute.
Merigó, JM & Casanovas, M 2011, 'A New Minkowski Distance Based on Induced Aggregation Operators', International Journal of Computational Intelligence Systems, vol. 4, no. 2, pp. 123-133.
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Merigó, JM & Casanovas, M 2011, 'Decision Making with Dempster-Shafer Theory Using Fuzzy Induced Aggregation Operators', Studies in Fuzziness and Soft Computing, vol. 265, pp. 209-228.
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We develop a new approach for decision making with Dempster-Shafer theory of evidence where the available information is uncertain and it can be assessed with fuzzy numbers. With this approach, we are able to represent the problem without losing relevant information, so the decision maker knows exactly which are the different alternatives and their consequences. For doing so, we suggest the use of different types of fuzzy induced aggregation operators in the problem. Then, we can aggregate the information considering all the different scenarios that could happen in the analysis. As a result, we get new types of fuzzy induced aggregation operators such as the belief structure - fuzzy induced ordered weighted averaging (BS-FIOWA) and the belief structure - fuzzy induced hybrid averaging (BS-FIHA) operator. We study some of their main properties. We further generalize this approach by using fuzzy induced generalized aggregation operators. We also develop an application of the new approach in a financial decision making problem about selection of financial strategies. © 2011 Springer-Verlag Berlin Heidelberg.
Merigó, JM & Casanovas, M 2011, 'Decision-making with distance measures and induced aggregation operators', Computers & Industrial Engineering, vol. 60, no. 1, pp. 66-76.
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Merigó, JM & Casanovas, M 2011, 'Induced aggregation operators in the Euclidean distance and its application in financial decision making', Expert Systems with Applications, vol. 38, no. 6, pp. 7603-7608.
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Merigó, JM & Casanovas, M 2011, 'Induced and uncertain heavy OWA operators', Computers & Industrial Engineering, vol. 60, no. 1, pp. 106-116.
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MERIGÓ, JM & CASANOVAS, M 2011, 'THE UNCERTAIN GENERALIZED OWA OPERATOR AND ITS APPLICATION TO FINANCIAL DECISION MAKING', International Journal of Information Technology & Decision Making, vol. 10, no. 02, pp. 211-230.
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We introduce the uncertain generalized OWA (UGOWA) operator. This operator is an extension of the OWA operator that uses generalized means and uncertain information represented as interval numbers. By using UGOWA, it is possible to obtain a wide range of uncertain aggregation operators such as the uncertain average (UA), the uncertain weighted average (UWA), the uncertain OWA (UOWA) operator, the uncertain ordered weighted geometric (UOWG) operator, the uncertain ordered weighted quadratic averaging (UOWQA) operator, the uncertain generalized mean (UGM), and many specialized operators. We study some of its main properties, and we further generalize the UGOWA operator using quasi-arithmetic means. The result is the Quasi-UOWA operator. We end the paper by presenting an application to a decision-making problem regarding the selection of financial strategies.
Merigó, JM & Casanovas, M 2011, 'The uncertain induced quasi-arithmetic OWA operator', International Journal of Intelligent Systems, vol. 26, no. 1, pp. 1-24.
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Merigo, JM & Gil-Lafuente, AM 2011, 'OWA OPERATORS IN HUMAN RESOURCE MANAGEMENT', ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, vol. 45, no. 2, pp. 153-168.
Merigó, JM & Gil-Lafuente, AM 2011, 'Decision-making in sport management based on the OWA operator', Expert Systems with Applications, vol. 38, no. 8, pp. 10408-10413.
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Merigó, JM & Gil-Lafuente, AM 2011, 'Fuzzy induced generalized aggregation operators and its application in multi-person decision making', Expert Systems with Applications, vol. 38, no. 8, pp. 9761-9772.
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Merigó, JM & Gil-Lafuente, AM 2011, 'Owa operators in human resource management', Economic Computation and Economic Cybernetics Studies and Research, vol. 2, pp. 118-134.
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We develop a new approach that uses the ordered weighted averaging (OWA) operator in different methods for the selection of human resources. The objective of this new model is to manipulate the neutrality of the old methods, so the decision maker can select human resources according to his degree of optimism or pessimism. In order to develop this model, first, a short revision of the OWA operators is introduced. Next, we briefly explain the general model for the selection of human resources and suggest three new indexes for the selection of human resources that use the OWA operator and the hybrid average in the Hamming distance, in the adequacy coefficient and in the index of maximum and minimum level. The main advantage of this method is that it is more complete than the previous ones so the decision maker gets a better understanding of the decision problem. The work ends with an illustrative example that shows the results obtained by using different types of aggregation operators in the new approaches.
Merigó, JM & Wei, G 2011, 'PROBABILISTIC AGGREGATION OPERATORS AND THEIR APPLICATION IN UNCERTAIN MULTI-PERSON DECISION-MAKING / TIKIMYBINIAI SUMAVIMO OPERATORIAI IR JŲ TAIKYMAS PRIIMANT GRUPINIUS SPRENDIMUS NEAPIBRĖŽTOJE APLINKOJE', Technological and Economic Development of Economy, vol. 17, no. 2, pp. 335-351.
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We present the uncertain probabilistic ordered weighted averaging (UPOWA) operator. It is an aggregation operator that uses probabilities and OWA operators in the same formulation considering the degree of importance of each concept in the analysis. Moreover, it also uses uncertain information assessed with interval numbers in the aggregation process. The main advantage of this aggregation operator is that it is able to use the attitudinal character of the decision maker and the available probabilistic information in an environment where the information is very imprecise and can be assessed with interval numbers. We study some of its main properties and particular cases such as the uncertain probabilistic aggregation (UPA) and the uncertain OWA (UOWA) operator. We also develop an application of the new approach in a multi-person decision-making problem in political management regarding the selection of monetary policies. Thus, we obtain the multiperson UPOWA (MP-UPOWA) operator. We see that this model gives more complete information of the decision problem because it is able to deal with decision making problems under uncertainty and under risk in the same formulation. Santrauka Autoriai pristato tikimybinį svertinio vidurkio operatorių, taikytiną neapibrežtumo sąlygomis. Tai tikimybėmis pagrįstas sumavimo operatorius, kuris kartu su svertinio vidurkio operatoriais gali įvertinti alternatyvų svarbumo laipsnį. Be to, jis gali operuoti neapibrežta informacija, išreikšta skaičiais intervaluose. Pagrindinis šio operatoriaus privalumas yra tas, kad jį galima taikyti uždaviniams, kuriuose informacija yra netiksli. Išnagrinėtos kai kurios minėto operatoriaus savybės. Sukurtas metodas pritaikytas monetarinei politikai parinkti, situacijai, kai sprendimus priima žmoniu grupė. Modelis suteikia išsamesnę informaciją apie problemą, nes gali įvertinti neapibrežtumus ir riziką.
Merigo, JM, Gil-Lafuente, AM & Gil-Aluja, J 2011, 'Decision making with the induced generalized adequacy coefficient', Applied and Computational Mathematics, vol. 10, no. 2, pp. 321-339.
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We introduce the induced generalized ordered weighted averaging adequacy coefficient (IGOWAAC) operator. The main advantage is that it provides a more complete generalization of the aggregation operators that includes a wide range of situations. We apply the new approach in a decision making problem.
Merigo, JM, Gil-Lafuente, AM & Gil-Aluja, J 2011, 'Soft computing techniques for decision making with induced aggregation operators', Information, vol. 14, no. 6, pp. 2019-2039.
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We present a method for decision making by using induced aggregation operators. This method is very useful for business decision making problems such as product management, investment selection and strategic management We introduce a new aggregation operator that uses the induced ordered weighted averaging (IOWA) operator in the adequacy coefficient. We call it the induced ordered weighted averaging adequacy coefficient (IOWAAC) operator. The main advantage is that it is able to deal with complex attitudinal characters in the aggregation process. Thus, we are able to give a better representation of the problem considering the complex environment that affects the decisions. We study some of the main properties of this approach and we see mat it includes the IOWA operator as a special case. We also see that sometimes this approach becomes the Hamming distance or more precisely, the induced OWA distance (IOWAD) operator. We further extend the IOWAAC operator by using the hybrid average, obtaining the induced hybrid averaging adequacy coefficient (IHAAC). We end the paper with a numerical example of the new approach in a decision making problem regarding product management. © 2011 International Information Institute.
Merigo, JM, Gil-Lafuente, AM, Zhou, L & Chenm, H 2011, 'Generalization of the linguistic aggregation operator and its application in decision making', Journal of Systems Engineering and Electronics, vol. 22, no. 4, pp. 593-603.
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Missmer, SA, Pearson, KR, Ryan, LM, Meeker, JD, Cramer, DW & Hauser, R 2011, 'Analysis of Multiple-cycle Data From Couples Undergoing In Vitro Fertilization Methodologic Issues and Statistical Approaches', EPIDEMIOLOGY, vol. 22, no. 4, pp. 497-504.
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The number of in vitro fertilization (IVF) cycles in the United States increased from fewer than 46,000 in 1995 to more than 120,000 in 2005. IVF and other assisted reproductive technology (ART) data are routinely collected and used to identify outcome predictors. However, researchers do not always make full use of the data due to their complexity. Design approaches have included restriction to first-cycle attempts only, which reduces power and identifies effects only of those factors associated with initial success. Many statistical techniques have been used or proposed for analysis of IVF data, ranging from simple t tests to sophisticated models designed specifically for IVF. We applied several of these methods to data from a prospective cohort of 2687 couples undergoing ART from 1994 through 2003. Results across methods are compared and the appropriateness of the various methods is discussed with the intent to illustrate methodologic validity. We observed a remarkable similarity of coefficient estimates across models. However, each method for dealing with multiple cycle data relies on assumptions that may or may not be expected to hold in a given IVF study. The robustness and reported magnitude of effect for individual predictors of IVF success may be inflated or attenuated due to violation of statistical assumptions, and should always be critically interpreted. Given that risk factors associated with IVF success may also advance our understanding of the physiologic processes underlying conception, implantation, and gestation, the application of valid methods to these complex data is critical. © 2011 by Lippincott Williams & Wilkins.
Movassaghi, S, Abolhasan, M & Lipman, J 2011, 'Addressing Schemes for Body Area Networks', IEEE COMMUNICATIONS LETTERS, vol. 15, no. 12, pp. 1310-1313.
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This letter explores address allocation in Body Area Networks (BANs) and proposes two novel schemes - Optimized Prophet Address Allocation (OPAA) and Hierarchical Collision-free Address Protocol (HCAP). The aim of the schemes is to use fewer bits in the address space, solve address wastage problems, reduce collisions and improve power efficiency. The usability and efficiency of the proposed schemes is shown through simulation and analysis. © 2006 IEEE.
Ngo, L, Ryan, LM, Mezzetti, M, Bois, FY & Smith, TJ 2011, 'Estimating metabolic rate for butadiene at steady state using a Bayesian physiologically-based pharmacokinetic model', ENVIRONMENTAL AND ECOLOGICAL STATISTICS, vol. 18, no. 1, pp. 131-146.
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In a study of 133 volunteer subjects, demographic, physiologic and pharmacokinetic data through exposure to 1,3-Butadiene (BD) were collected in order to estimate the percentage of BD concentration metabolized at steady state, and to determine whether this percentage varies across gender, racial, and age groups. During the 20 min of continuous exposure to 2 parts per million (ppm) of BD, five measurements of exhaled concentration were made on each subject. In the following 40 min washout period, another five measurements were collected. A Bayesian hierarchical compartmental physiologically-based pharmacokinetic model (PKPB) was used. Using prior information on the model parameters, Markov Chain Monte Carlo (MCMC) simulation was conducted to obtain posterior distributions. The overall estimate of the mean percent of BD metabolized at steady state was 12.7% (95% credible interval: 7.7-17.8%). There was no significant difference in gender with males having a mean of 13. 5%, and females 12.3%. Among the racial groups, Hispanic (13.9%), White (13.0%), Asian (12.1%), and Black (10.9%), the significant difference came from the difference between Black and Hispanic with a 95% credible interval from -5.63 to -0.30%. Those older than 30 years had a mean of 12.2% versus 12.9% for the younger group; although this was not a statistically significant difference. Given a constant inhalation input of 2 ppm, at steady state, the overall mean exhaled concentration was estimated to be 1.75ppm (95% credible interval: 1.64-1.84). An equivalent parameter, first-order metabolic rate constant, was also estimated and found to be consistent with the percent of BD metabolized at steady state across gender, race, and age strata. © 2009 Springer Science+Business Media, LLC.
Otoom, AF, Gunes, H, Concha, OP & Piccardi, M 2011, 'MLiT: mixtures of Gaussians under linear transformations', PATTERN ANALYSIS AND APPLICATIONS, vol. 14, no. 2, pp. 193-205.
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The curse of dimensionality hinders the effectiveness of density estimation in high dimensional spaces. Many techniques have been proposed in the past to discover embedded, locally linear manifolds of lower dimensionality, including the mixture of principal component analyzers, the mixture of probabilistic principal component analyzers and the mixture of factor analyzers. In this paper, we propose a novel mixture model for reducing dimensionality based on a linear transformation which is not restricted to be orthogonal nor aligned along the principal directions. For experimental validation, we have used the proposed model for classification of five 'hard' data sets and compared its accuracy with that of other popular classifiers. The performance of the proposed method has outperformed that of the mixture of probabilistic principal component analyzers on four out of the five compared data sets with improvements ranging from 0. 5 to 3.2%. Moreover, on all data sets, the accuracy achieved by the proposed method outperformed that of the Gaussian mixture model with improvements ranging from 0.2 to 3.4%. © 2011 Springer-Verlag London Limited.
Paisitkriangkrai, S, Mei, T, Zhang, J & Hua, X-S 2011, 'Clip-based hierarchical representation for near-duplicate video detection', International Journal of Computer Mathematics, vol. 88, no. 18, pp. 3817-3833.
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Ruta, D, Gabrys, B & Lemke, C 2011, 'A Generic Multilevel Architecture for Time Series Prediction', IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 3, pp. 350-359.
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Sim, K, Liu, G, Gopalkrishnan, V & Li, J 2011, 'A case study on financial ratios via cross-graph quasi-bicliques', Information Sciences, vol. 181, no. 1, pp. 201-216.
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Stocks with similar financial ratio values across years have similar price movements. We investigate this hypothesis by clustering groups of stocks that exhibit homogeneous financial ratio values across years, and then study their price movements. We propose using cross-graph quasi-biclique (CGQB) subgraphs to cluster stocks, as they can define the three dimensional (3D) subspaces of financial ratios that the stocks are homogeneous in across the years, and they can also handle missing values that are rampant in the stock data. Furthermore, investors can easily analyze these 3D subspaces to explore the relations between the stocks and financial ratios. We develop a novel algorithm, CGQBminer, which mines the complete set of CGQB subgraphs from the stock data. Through experimental analysis, we show that the hypothesis is valid. Furthermore, we demonstrate that having an investment strategy which uses groups of stocks mined by CGQB subgraphs have higher returns than one that does not. We also conducted an extensive performance analysis on CGQBminer, and show that it is efficient across different 3D datasets and parameter settings. © 2010 Elsevier Inc. All rights reserved.
Sparks, R, Carter, C, Graham, P, Muscatello, D, Churches, T, Kaldor, J, Turner, R, Zheng, W & Ryan, L 2011, 'Understanding sources of variation in syndromic surveillance for early warning of natural or intentional disease outbreaks (vol 42, pg 613, 2010)', IIE TRANSACTIONS, vol. 43, no. 3, pp. 231-231.
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Suglia, SF, Ryan, L, Bellinger, DC, Enlow, MB & Wright, RJ 2011, 'Children's Exposure to Violence and Distress Symptoms: Influence of Caretakers' Psychological Functioning', INTERNATIONAL JOURNAL OF BEHAVIORAL MEDICINE, vol. 18, no. 1, pp. 35-43.
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Background: Previous studies linking violence exposure to adverse child behavior have typically relied on parental report of child symptoms without accounting for the informant's mental well-being, despite evidence that parental mental health can influence children's mental health and the parent's report of distress symptoms. Purpose: We assess the influence of maternal depression on the violence exposure and child distress association in a subset of the Maternal Infant Smoking Study of East Boston, a prospective birth cohort. Methods: Mothers reported on their children's violence exposure using the Survey of Children's Exposure to Community Violence (ETV) and completed the Checklist of Child Distress Symptoms (CCDS). The children also completed the ETV survey and the self-report version of the CCDS. Linear regression was used to assess the influence of violence exposure on distress symptoms adjusting for potential confounders, first using parent's report of exposure and outcome and a second time using the child's self-report. The mediating effect of maternal depression on the violence and distress association was also tested. Results: Among the 162 children ages 7 to 11, 51% were boys and 43% self-identified as Hispanic. When using child self-report, increased violence exposure was significantly associated with a broader range of distress symptoms (numbness, arousal, intrusion, avoidance subscales) compared to parent reported findings, which were only significantly related to the intrusion and avoidance subscales. Moreover, a significant mediation effect of maternal depression on the violence and distress association was noted only when mother's report of exposure and outcome was used. Conclusion: Considering both parent and child self-report of violence is necessary to obtain a complete picture of violence exposure because parents and children may be offering different, although equally valid information. The influence of maternal depressive symptoms ...
Tang, M, Zhou, Y, Li, J, Wang, W, Cui, P, Hou, Y, Luo, Z, Li, J, Lei, F & Yan, B 2011, 'Exploring the wild birds’ migration data for the disease spread study of H5N1: a clustering and association approach', Knowledge and Information Systems, vol. 27, no. 2, pp. 227-251.
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Knowledge about the wetland use of migratory bird species during the annual life circle is very interesting to biologists, as it is critically important in many decision-making processes such as for conservation site construction and avian influenza control. The raw data of the habitat areas and the migration routes are usually in large scale and with high complexity when they are determined by high-tech GPS satellite telemetry. In this paper, we convert these biological problems into computational studies and introduce efficient algorithms for the data analysis. Our key idea is the concept of hierarchical clustering for migration habitat localizations, and the notion of association rules for the discovery of migration routes from the scattered location points in the GIS. One of our clustering results is a tree structure, specially called spatial-tree, which is an illusive map depicting the breeding and wintering home range of bar-headed geese. A related result to this observation is an association pattern that reveals a high possibility that bar-headed geese's potential autumn migration routes are likely between the breeding sites in the Qinghai Lake, China and the wintering sites in Tibet river valley. Given the susceptibility of geese to spread H5N1, and on the basis of the chronology and the rates of the bar-headed geese migration movements, we can conjecture that bar-headed geese play an important role in the spread of the H5N1 virus at a regional scale in Qinghai-Tibetan Plateau. © 2010 Springer-Verlag London Limited.
Yang, T, Kecman, V, Cao, L, Zhang, C & Zhexue Huang, J 2011, 'Margin-based ensemble classifier for protein fold recognition', Expert Systems with Applications, vol. 38, no. 10, pp. 12348-12355.
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Recognition of protein folding patterns is an important step in protein structure and function predictions. Traditional sequence similarity-based approach fails to yield convincing predictions when proteins have low sequence identities, while the taxonom
Zeng, T, Li, J & Liu, J 2011, 'Distinct interfacial biclique patterns between ssDNA‐binding proteins and those with dsDNAs', Proteins: Structure, Function, and Bioinformatics, vol. 79, no. 2, pp. 598-610.
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AbstractWe introduce a new motif called interfacial biclique pattern to study the difference between double‐stranded DNA‐binding proteins (DSBs, most of them also known to play the role as transcriptional factors) and single‐stranded DNA‐binding proteins (SSBs) which are found to involve in many applications recently. An interfacial biclique pattern in a protein‐DNA complex usually consists of a group of residues and a group of nucleotides such that every residue has a contact to all of the bases. The proposal of this idea is based on a biological redundancy mechanism that: a site mutation has little influence on the other residues to recognize the target nucleotides and vice versa. The distribution of the residues on the interfacial motifs is investigated to identify distinct stable preferred residues, stable un‐preferred residues and unstable preferred residues between SSBs and DSBs. We also examine residue co‐occurrence and residue‐base association rules in the interfacial motifs to uncover the different choices of residue combinations by SSBs and DSBs that have contacts with one or more bases. We found that DSBs and SSBs have their own right residues at the right places for the binding preference and association with nucleotides. Some of our results can be supported by literature work. Proteins 2011. © 2010 Wiley‐Liss, Inc.
Zhao, L, Wong, L & Li, J 2011, 'Antibody-Specified B-Cell Epitope Prediction in Line with the Principle of Context-Awareness', IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, vol. 8, no. 6, pp. 1483-1494.
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Context-awareness is a characteristic in the recognition between antigens and antibodies, highlighting the reconfiguration of epitope residues when an antigen interacts with a different antibody. A coarse binary classification of antigen regions into epitopes, or nonepitopes without specifying antibodies may not accurately reflect this biological reality. Therefore, we study an antibody-specified epitope prediction problem in line with this principle. This problem is new and challenging as we pinpoint a subset of the antigenic residues from an antigen when it binds to a specific antibody. We introduce two kinds of associations of the contextual awareness: 1) residues-residues pairing preference, and 2) the dependence between sets of contact residue pairs. Preference plays a bridging role to link interacting paratope and epitope residues while dependence is used to extend the association from one-dimension to two-dimension. The paratope/epitope residues' relative composition, cooperativity ratios, and Markov properties are also utilized to enhance our method. A nonredundant data set containing 80 antibody-antigen complexes is compiled and used in the evaluation. The results show that our method yields a good performance on antibody-specified epitope prediction. On the traditional antibody-ignored epitope prediction problem, a simplified version of our method can produce a competitive, sometimes much better, performance in comparison with three structure-based predictors. © 2011 IEEE.
Zheng, L, He, X, Wu, Q & Samali, B 2011, 'A system for licence plate recognition using a hierarchically combined classifier', International Journal of Intelligent Systems Technologies and Applications, vol. 10, no. 2, pp. 189-189.
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In a real time, automatic licence plate recognition system, licence detection, character segmentation and character recognition are three important components. All these three components generally require high accuracy and fast recognition speed to process. In this paper, general processing steps for license plate recognition (LPR) are addressed. After three types of combined classifiers are introduced and compared, a hierarchically combined classifier is designed based on an inductive learning-based method and an support vector machine (SVM)-based classification. This approach employs the inductive learning-based method to roughly divide all classes into smaller groups. Then, the SVM approach is used for character classification in individual groups. Having obtained a collection of samples of characters in advance from licence plates after licence detection and character segmentation steps, some known samples are available for training. After the training process, the inductive learning rules are extracted for rough classification and the parameters used for SVM-based classification are obtained. Then, a classification tree is constructed for next fast training and testing processes based on SVMs. The experimental results show that the hierarchically combined classifier is better than either the inductive learning-based classification or the SVM-based classification with a lower error rate and a faster processing speed. © 2011 Inderscience Enterprises Ltd.
Zhu, X, Ding, W, Yu, PS & Zhang, C 2011, 'One-class learning and concept summarization for data streams', Knowledge and Information Systems, vol. 28, no. 3, pp. 523-553.
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In this paper, we formulate a new research problem of concept learning and summarization for one-class data streams. The main objectives are to (1) allow users to label instance groups, instead of single instances, as positive samples for learning, and (2) summarize concepts labeled by users over the whole stream. The employment of the batch-labeling raises serious issues for stream-oriented concept learning and summarization, because a labeled instance group may contain non-positive samples and users may change their labeling interests at any time. As a result, so the positive samples labeled by users, over the whole stream, may be inconsistent and contain multiple concepts. To resolve these issues, we propose a one-class learning and summarization (OCLS) framework with two major components.
Zhu, X, Zhang, X, Peng, J, Chen, X & Li, J 2011, 'Photonic crystal fibers for supercontinuum generation', Frontiers of Optoelectronics in China, vol. 4, no. 4, pp. 415-419.
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Zong, Y, Xu, G, Jin, P, Zhang, Y & Chen, E 2011, 'HC_AB: A new heuristic clustering algorithm based on Approximate Backbone', Information Processing Letters, vol. 111, no. 17, pp. 857-863.
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Clustering is an important research area with numerous applications in pattern recognition, machine learning, and data mining. Since the clustering problem on numeric data sets can be formulated as a typical combinatorial optimization problem, many researches have addressed the design of heuristic algorithms for finding sub-optimal solutions in a reasonable period of time. However, most of the heuristic clustering algorithms suffer from the problem of being sensitive to the initialization and do not guarantee the high quality results. Recently, Approximate Backbone (AB), i.e., the commonly shared intersection of several sub-optimal solutions, has been proposed to address the sensitivity problem of initialization. In this paper, we aim to introduce the AB into heuristic clustering to overcome the initialization sensitivity of conventional heuristic clustering algorithms. The main advantage of the proposed method is the capability of restricting the initial search space around the optimal result by defining the AB, and in turn, reducing the impact of initialization on clustering, eventually improving the performance of heuristic clustering. Experiments on synthetic and real world data sets are performed to validate the effectiveness of the proposed approach in comparison to three conventional heuristic clustering algorithms and three other algorithms with improvement on initialization
Zong, Y, Xu, GD, Zhang, YC & Li, MC 2011, 'Node priority guided clustering algorithm', Kongzhi yu Juece/Control and Decision, vol. 26, no. 6.
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Density-based clustering algorithms have the advantages of clustering with arbitrary shapes and handling noise data, but cannot deal with unsymmetrical density distribution and high dimensionality dataset. Therefore, a node priority guided clustering algorithm(NPGC) is proposed. A direct K neighbor graph of dataset is set up based on KNN neighbor method. Then the local information of each node in graph is captured by using KNN kernel density estimate method, and the node priority is calculated by passing the local information through graph. Finally, a depth-first search on graph is applied to find out the clustering results based on the local kernel degree. Experiment results show that NPGC has the ability to deal with unsymmetrical density distribution and high dimensionality dataset.
Anaissi, A, Kennedy, PJ & Goyal, M 1970, 'Dimension reduction of microarray data based on local principal component', World Academy of Science, Engineering and Technology, pp. 1176-1181.
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Analysis and visualization of microarraydata is veryassistantfor biologists and clinicians in the field of diagnosis and treatment of patients. It allows Clinicians to better understand the structure of microarray and facilitates understanding gene expression in cells. However, microarray dataset is a complex data set and has thousands of features and a very small number of observations. This very high dimensional data set often contains some noise, non-useful information and a small number of relevant features for disease or genotype. This paper proposes a non-linear dimensionality reduction algorithm Local Principal Component (LPC) which aims to maps high dimensional data to a lower dimensional space. The reduced data represents the most important variables underlying the original data. Experimental results and comparisons are presented to show the quality of the proposed algorithm. Moreover, experiments also show how this algorithm reduces high dimensional data whilst preserving the neighbourhoods of the points in the low dimensional space as in the high dimensional space.
Anaissi, A, Kennedy, PJ & Goyal, M 1970, 'Feature Selection of Imbalanced Gene Expression Microarray Data', 2011 12th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Distributed Computing, IEEE, Sydney, pp. 73-78.
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Gene expression data is a very complex data set characterised by abundant numbers of features but with a low number of observations. However, only a small number of these features are relevant to an outcome of interest. With this kind of data set, feature selection becomes a real prerequisite. This paper proposes a methodology for feature selection for an imbalanced leukaemia gene expression data based on random forest algorithm. It presents the importance of feature selection in terms of reducing the number of features, enhancing the quality of machine learning and providing better understanding for biologists in diagnosis and prediction. Algorithms are presented to show the methodology and strategy for feature selection taking care to avoid overfitting. Moreover, experiments are done using imbalanced Leukaemia gene expression data and special measurement is used to evaluate the quality of feature selection and performance of classification.
Apeh, E & Gabrys, B 1970, 'Change Mining of Customer Profiles Based on Transactional Data', 2011 IEEE 11th International Conference on Data Mining Workshops, 2011 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, pp. 560-567.
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Customer transactions tend to change overtime with changing customer behaviour patterns. Classifier models, however, are often designed to perform prediction on data which is assumed to be static. These classifier models thus deteriorate in performance overtime when predicting in the context of evolving data. Robust adaptive classification models are therefore needed to detect and adjust to the kind of changes that are common in transactional data. This paper presents an investigation into using change mining to monitor the adaptive classification of customers based on their transactions through a moving time window. Results from our experiments show that our approach can be used for learning and adapting to changing customer profiles. © 2011 IEEE.
Apeh, ET, Gabrys, B & Schierz, A 1970, 'Customer profile classification using transactional data', 2011 Third World Congress on Nature and Biologically Inspired Computing, 2011 Third World Congress on Nature and Biologically Inspired Computing (NaBIC), IEEE, pp. 37-43.
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Customer profiles are by definition made up of factual and transactional data. It is often the case that due to reasons such as high cost of data acquisition and/or protection, only the transactional data are available for data mining operations. Transactional data, however, tend to be highly sparse and skewed due to a large proportion of customers engaging in very few transactions. This can result in a bias in the prediction accuracy of classifiers built using them towards the larger proportion of customers with fewer transactions. This paper investigates an approach for accurately and confidently grouping and classifying customers in bins on the basis of the number of their transactions. The experiments we conducted on a highly sparse and skewed real-world transactional data show that our proposed approach can be used to identify a critical point at which customer profiles can be more confidently distinguished. © 2011 IEEE.
Beck, D, Brandl, M, Pham, TD, Chang, C-C, Zhou, X, Pham, TD, Zhou, X, Tanaka, H, Oyama-Higa, M, Jiang, X, Sun, C, Kowalski, J & Jia, X 1970, 'In-Silico Identification Of Micro-Loops In Myelodysplastic Syndromes', AIP Conference Proceedings, 2011 INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL MODELS FOR LIFE SCIENCES (CMLS-11), AIP, pp. 263-271.
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Micro-loops are regulatory network motifs that leverage transcriptional and posttranscriptional control to effectively regulate the transcriptome. In this paper a regulatory network for Myelodysplastic Syndromes (MDSs) was constructed from the literature and publicly available data sources. The network was filtered using data from deep-sequencing of small RNAs, exon and microarrays. Motif discovery showed that micro-loops might exist in MDS. We further used the identified micro-loops and performed basic network analysis to identify the known disease gene RUNX1/AML, as well as miRNA family hsa-mir-181. This suggested that the concept of micro-loops can be applied to enhance disease gene identification and biomarker discovery. © 2011 American Institute of Physics.
Borzeshi, EZ, Piccardi, M & Xu, RYD 1970, 'A discriminative prototype selection approach for graph embedding in human action recognition', 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), IEEE, Barcelona Spain, pp. 1295-1301.
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This paper proposes a novel graph-based method for representing a human's shape during the performance of an action. Despite their strong representational power, graphs are computationally cumbersome for pattern analysis. One way of circumventing this problem is that of transforming the graphs into a vector space by means of graph embedding. Such an embedding can be conveniently obtained by way of a set of 'prototype' graphs and a dissimilarity measure: yet, the critical step in this approach is the selection of a suitable set of prototypes which can capture both the salient structure within each action class as well as the intra-class variation. This paper proposes a new discriminative approach for the selection of prototypes which maximizes a function of the inter- and intra-class distances. Experiments on an action recognition dataset reported in the paper show that such a discriminative approach outperforms well-established prototype selection methods such as center, border and random prototype selection.
Borzeshi, EZ, Xu, R & Piccardi, M 1970, 'Automatic Human Action Recognition in Videos by Graph Embedding', Lecture Notes in Computer Science.Image Analysis and Processing - ICIAP 2011.16th International Conference Part II, International Conference on Image Analysis and Processing, Springer Berlin Heidelberg, Ravenna, Italy, pp. 19-28.
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The problem of human action recognition has received increasing attention in recent years for its importance in many applications. Yet, the main limitation of current approaches is that they do not capture well the spatial relationships in the subject performing the action. This paper presents an initial study which uses graphs to represent the actorâs shape and graph embedding to then convert the graph into a suitable feature vector. In this way, we can benefit from the wide range of statistical classifiers while retaining the strong representational power of graphs. The paper shows that, although the proposed method does not yet achieve accuracy comparable to that of the best existing approaches, the embedded graphs are capable of describing the deformable human shape and its evolution along the time. This confirms the interesting rationale of the approach and its potential for future performance.
Borzeshi, EZ, Xu, R & Piccardi, M 1970, 'Automatic Human Action Recognition in Videos by Graph Embedding', IMAGE ANALYSIS AND PROCESSING - ICIAP 2011, PT II, 16th International Conference on Image Analysis and Processing (ICIAP), SPRINGER-VERLAG BERLIN, Ravenna, ITALY, pp. 19-28.
Brandl, MB, Beck, D, Pham, TD, Pham, TD, Zhou, X, Tanaka, H, Oyama-Higa, M, Jiang, X, Sun, C, Kowalski, J & Jia, X 1970, 'Application of Fuzzy c-Means and Joint-Feature-Clustering to Detect Redundancies of Image-Features in Drug Combinations Studies of Breast Cancer', AIP Conference Proceedings, 2011 INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL MODELS FOR LIFE SCIENCES (CMLS-11), AIP, pp. 65-72.
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The high dimensionality of image-based dataset can be a drawback for classification accuracy. In this study, we propose the application of fuzzy c-means clustering, cluster validity indices and the notation of a joint-feature-clustering matrix to find redundancies of image-features. The introduced matrix indicates how frequently features are grouped in a mutual cluster. The resulting information can be used to find data-derived feature prototypes with a common biological meaning, reduce data storage as well as computation times and improve the classification accuracy. © 2011 American Institute of Physics.
Casanovas, M & Merigo, JM 1970, 'A new decision making method with uncertain induced aggregation operators', 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MDCM), 2011 Ieee Symposium On Computational Intelligence In Multicriteria Decision-Making - Part Of 17273 - 2011 Ssci, IEEE, pp. 151-158.
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We introduce a new decision making method based on the use of uncertain induced aggregation operators, probabilities and weighted averages. We present the uncertain induced probabilistic ordered weighted averaging weighted averaging (UIPOWAWA) operator. It is a new aggregation operator that provides a parameterized family of aggregation operators between the minimum and the maximum in a unified framework between the probability, the weighted average and the induced ordered weighted averaging (IOWA) operator. Moreover, it also uses uncertain information that can be represented with interval numbers. We study some of its main properties and particular cases including the uncertain induced probabilistic OWA (UIPOWA), the uncertain induced OWA (UIOWA) and the uncertain weighted average (UWA). We also develop an application in multi-person decision making regarding the selection of fiscal policies. © 2011 IEEE.
Chen, L & Zhang, C 1970, 'Semi-supervised Variable Weighting for Clustering', Proceedings of the 2011 SIAM International Conference on Data Mining, Proceedings of the 2011 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, Mesa, Arizona, USA, pp. 863-871.
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Semi-supervised learning, which uses a small amount of labeled data in conjunction with a large amount of unlabeled data for training, has recently attracted huge research attention due to the considerable improvement in learning accuracy. In this work, we focus on semi- supervised variable weighting for clustering, which is a critical step in clustering as it is known that interesting clustering structure usually occurs in a subspace defined by a subset of variables. Besides exploiting both labeled and unlabeled data to effectively identify the real importance of variables, our method embeds variable weighting in the process of semi-supervised clustering, rather than calculating variable weights separately, to ensure the computation efficiency. Our experiments carried out on both synthetic and real data demonstrate that semi-supervised variable weighting signicantly improves the clustering accuracy of existing semi-supervised k-means without variable weighting, or with unsupervised variable weighting.
Chen, X, He, X, Yang, J & Wu, Q 1970, 'An effective document image deblurring algorithm', CVPR 2011, 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Colorado Springs, pp. 369-376.
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Deblurring camera-based document image is an important task in digital document processing, since it can improve both the accuracy of optical character recognition systems and the visual quality of document images. Traditional deblurring algorithms have been proposed to work for natural-scene images. However the natural-scene images are not consistent with document images. In this paper, the distinct characteristics of document images are investigated. We propose a content-aware prior for document image deblurring. It is based on document image foreground segmentation. Besides, an upper-bound constraint combined with total variation based method is proposed to suppress the rings in the deblurred image. Comparing with the traditional general purpose deblurring methods, the proposed deblurring algorithm can produce more pleasing results on document images. Encouraging experimental results demonstrate the efficacy of the proposed method. © 2011 IEEE.
Concha, OP, Da Xu, RY, Moghaddam, Z & Piccardi, M 1970, 'HMM-MIO: An enhanced hidden Markov model for action recognition', CVPR 2011 WORKSHOPS, 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops), IEEE, Colorado Spring, CO, pp. 62-69.
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Generative models can be flexibly employed in a variety of tasks such as classification, detection and segmentation thanks to their explicit modelling of likelihood functions. However, likelihood functions are hard to model accurately in many real cases. In this paper, we present an enhanced hidden Markov model capable of dealing with the noisy, high-dimensional and sparse measurements typical of action feature sets. The modified model, named hidden Markov model with multiple, independent observations (HMM-MIO), joins: a) robustness to observation outliers, b) dimensionality reduction, and c) processing of sparse observations. In the paper, a set of experimental results over the Weizmann and KTH datasets shows that this model can be tuned to achieve classification accuracy comparable to that of discriminative classifiers. While discriminative approaches remain the natural choice for classification tasks, our results prove that likelihoods, too, can be modelled to a high level of accuracy. In the near future, we plan extension of HMM-MIO along the lines of infinite Markov models and its integration into a switching model for continuous human action recognition. © 2011 IEEE.
Dong, X, Zheng, Z, Cao, L, Zhao, Y, Zhang, C, Li, J, Wei, W & Ou, Y 1970, 'e-NSP: efficient negative sequential pattern mining based on identified positive patterns without database rescanning.', CIKM, ACM International Conference on Information and Knowledge Management, ACM, Glasgow, Scotland, UK, pp. 825-830.
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Mining Negative Sequential Patterns (NSP) is much more challenging than mining Positive Sequential Patterns (PSP) due to the high computational complexity and huge search space required in calculating Negative Sequential Candidates (NSC). Very few approaches are available for mining NSP, which mainly rely on re-scanning databases after identifying PSP. As a result, they are very inefficient. In this paper, we propose an efficient algorithm for mining NSP, called e-NSP, which mines for NSP by only involving the identified PSP, without re-scanning databases. First, negative containment is defined to determine whether or not a data sequence contains a negative sequence. Second, an efficient approach is proposed to convert the negative containment problem to a positive containment problem. The supports of NSC are then calculated based only on the corresponding PSP. Finally, a simple but efficient approach is proposed to generate NSC. With e-NSP, mining NSP does not require additional database scans, and the existing PSP mining algorithms can be integrated into e-NSP to mine for NSP efficiently. e-NSP is compared with two currently available NSP mining algorithms on 14 synthetic and real-life datasets. Intensive experiments show that e-NSP takes as little as 3% of the runtime of the baseline approaches and is applicable for efficient mining of NSP in large datasets. © 2011 ACM.
Durao, F, Bayyapu, K, Guandong Xu, Dolog, P & Lage, R 1970, 'Using Tag-Neighbors for Query Expansion in Medical Information Retrieval', 2011 International Conference on Information Science and Applications, 2011 International Conference on Information Science and Applications (ICISA 2011), IEEE, Jeju Island, South Korea.
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In the context of medical document retrieval, users often under-specified queries lead to undesired search results that suffer from not containing the information they seek, inadequate domain knowledge matches and unreliable sources. To overcome the limitations of under-specified queries, we utilize tags to enhance information retrieval capabilities by expanding users' original queries with context-relevant information. We compute a set of significant tag neighbor candidates based on the neighbor frequency and weight, and utilize the most frequent and weighted neighbors to expand an entry query that has terms matching tags. The proposed approach is evaluated using MedWorm medical article collection and standard evaluation methods from the text retrieval conference (TREC). We compared the baseline of 0.353 for Mean Average Precision (MAP), reaching a MAP 0.491 (+39%) with the query expansion. In-depth analysis shows how this strategy is beneficial when compared with different ranks of the retrieval results. © 2011 IEEE.
Eastwood, M & Gabrys, B 1970, 'Model level combination of tree ensemble hyperboxes via GFMM', 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011), IEEE, pp. 443-447.
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An ensemble of decision trees defines an overlapping set of hyperboxes. These hyperboxes in turn define a disjoint set of hyperboxes each with an associated vector of individual decisions. These vectors can be used to robustly label the hyperboxes by class, or to define soft labels. We sample from these hyperboxes and use them to build a single classifier within the General Fuzzy Min-Max (GFMM) framework that gains information from many different resamplings of the data through the ensemble from which it is built. This method is found to build robust GFMM models, with improved performance on most datasets compared to the basic GFMM. © 2011 IEEE.
Fu, Y, Li, B, Zhu, X & Zhang, C 1970, 'Do they belong to the same class', Proceedings of the 20th ACM international conference on Information and knowledge management, CIKM '11: International Conference on Information and Knowledge Management, ACM, Glasgow, Scotland, pp. 2161-2164.
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Traditional active learning methods request experts to provide ground truths to the queried instances, which can be expensive in practice. An alternative solution is to ask nonexpert labelers to do such labeling work, which can not tell the definite class labels. In this paper, we propose a new active learning paradigm, in which a nonexpert labeler is only asked "whether a pair of instances belong to the same class". To instantiate the proposed paradigm, we adopt the MinCut algorithm as the base classifier. We first construct a graph based on the pairwise distance of all the labeled and unlabeled instances and then repeatedly update the unlabeled edge weights on the max-flow paths in the graph. Finally, we select an unlabeled subset of nodes with the highest prediction confidence as the labeled data, which are included into the labeled data set to learn a new classifier for the next round of active learning. The experimental results and comparisons, with state-of-the-art methods, demonstrate that our active learning paradigm can result in good performance with nonexpert labelers
Ghous, H, Ho, N, Catchpoole, DR & Kennedy, PJ 1970, 'Comparing functional visualizations of genes', The 5th International Workshop on Data Mining in Functional Genomics and Proteomics: Current Trends and Future Directions, International Workshop on Data Mining in Functional Genomics and Proteomics: Current Trends and Future Directions, European Conference on Machine Learning, Athens, Greece, pp. 12-21.
Juszczyszyn, K, Budka, M & Musial, K 1970, 'The Dynamic Structural Patterns of Social Networks Based on Triad Transitions', 2011 International Conference on Advances in Social Networks Analysis and Mining, 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2011), IEEE, Kaohsiung, TAIWAN, pp. 581-586.
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Juszczyszyn, K, Musial, K & Budka, M 1970, 'Link Prediction Based on Subgraph Evolution in Dynamic Social Networks', 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust (PASSAT) / 2011 IEEE Third Int'l Conference on Social Computing (SocialCom), IEEE, pp. 27-34.
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We propose a new method for characterizing the dynamics of complex networks with its application to the link prediction problem. Our approach is based on the discovery of network subgraphs (in this study: triads of nodes) and measuring their transitions during network evolution. We define the Triad Transition Matrix (TTM) containing the probabilities of transitions between triads found in the network, then we show how it can help to discover and quantify the dynamic patterns of network evolution. We also propose the application of TTM to link prediction with an algorithm (called TTM-predictor) which shows good performance, especially for sparse networks analyzed in short time scales. The future applications and research directions of our approach are also proposed and discussed. © 2011 IEEE.
Kaidonis, MA & Stoianoff, NP 1970, 'Legislation, Citizens’ Rights, and the Self-Determination of a Developing Country: A Papua New Guinean Case Study', IUCN Academy of Environmental Law 2006 Colloquium, Implementing Environmental Legislation: The Critical Role of Enhancement and Compliance, Edward Elgar Publishing, Pace University School of Law in White Plains, New York.
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Kazienko, P, Kukla, E, Musial, K, Kajdanowicz, T, Bródka, P & Gaworecki, J 1970, 'A Generic Model for a Multidimensional Temporal Social Network', E-TECHNOLOGIES AND NETWORKS FOR DEVELOPMENT, 1st International Conference on e-Technologies and Networks for Development (ICeND 2011), Springer Berlin Heidelberg, Inst Finance Management, Dar es Salaam, TANZANIA, pp. 1-14.
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Kazienko, P, Musial, K, Kukla, E, Kajdanowicz, T & Bródka, P 1970, 'Multidimensional Social Network: Model and Analysis', COMPUTATIONAL COLLECTIVE INTELLIGENCE: TECHNOLOGIES AND APPLICATIONS, PT I, 3rd International Conference on Computational Collective Intelligence (ICCCI 2011), Springer Berlin Heidelberg, Gdynia Maritime Univ, Gdynia, POLAND, pp. 378-387.
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Khan, L, Pechenizkiy, M, Zliobaite, I, Agrawal, C, Bifet, A, Delany, SJ, Dries, A, Fan, W, Gabrys, B, Gama, J, Gao, J, Gopalkrishnan, V, Holmes, G, Katakis, I, Kuncheva, L, Van Leeuwen, M, Masud, M, Menasalvas, E, Minku, L, Pfahringer, B, Polikar, R, Rodrigues, PP, Tsoumakas, G & Tsymbal, A 1970, 'Preface to the Handling Concept Drift and Reoccurring Contexts in Adaptive Information Systems Workshop', 2011 IEEE 11th International Conference on Data Mining Workshops, 2011 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE.
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Khoshgoftaar, TM & Zhu, X 1970, 'Preface', 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence (ICTAI), IEEE.
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Kusakunniran, W, Wu, Q, Zhang, J & Li, H 1970, 'Pairwise Shape configuration-based PSA for gait recognition under small viewing angle change', 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, Klagenfurt, Austria, pp. 17-22.
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Two main components of Procrustes Shape Analysis (PSA) are adopted and adapted specifically to address gait recognition under small viewing angle change: 1) Procrustes Mean Shape (PMS) for gait signature description; 2) Procrustes Distance (PD) for similarity measurement. Pairwise Shape Configuration (PSC) is proposed as a shape descriptor in place of existing Centroid Shape Configuration (CSC) in conventional PSA. PSC can better tolerate shape change caused by viewing angle change than CSC. Small variation of viewing angle makes large impact only on global gait appearance. Without major impact on local spatio-temporal motion, PSC which effectively embeds local shape information can generate robust view-invariant gait feature. To enhance gait recognition performance, a novel boundary re-sampling process is proposed. It provides only necessary re-sampled points to PSC description. In the meantime, it efficiently solves problems of boundary point correspondence, boundary normalization and boundary smoothness. This re-sampling process adopts prior knowledge of body pose structure. Comprehensive experiment is carried out on the CASIA gait database. The proposed method is shown to significantly improve performance of gait recognition under small viewing angle change without additional requirements of supervised learning, known viewing angle and multi-camera system, when compared with other methods in literatures. © 2011 IEEE.
Kusakunniran, W, Wu, Q, Zhang, J & Li, H 1970, 'Speed-invariant gait recognition based on Procrustes Shape Analysis using higher-order shape configuration', 2011 18th IEEE International Conference on Image Processing, 2011 18th IEEE International Conference on Image Processing (ICIP 2011), IEEE, Brussels, Belgium, pp. 545-548.
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Walking speed change is considered a typical challenge hindering reliable human gait recognition. This paper proposes a novel method to extract speed-invariant gait feature based on Procrustes Shape Analysis (PSA). Two major components of PSA, i.e., Procrustes Mean Shape (PMS) and Procrustes Distance (PD), are adopted and adapted specifically for the purpose of speed-invariant gait recognition. One of our major contributions in this work is that, instead of using conventional Centroid Shape Configuration (CSC) which is not suitable to describe individual gait when body shape changes particularly due to change of walking speed, we propose a new descriptor named Higher-order derivative Shape Configuration (HSC) which can generate robust speed-invariant gait feature. From the first order to the higher order, derivative shape configuration contains gait shape information of different levels. Intuitively, the higher order of derivative is able to describe gait with shape change caused by the larger change of walking speed. Encouraging experimental results show that our proposed method is efficient for speed-invariant gait recognition and evidently outperforms other existing methods in the literatures. © 2011 IEEE.
Li, B, Zhu, X, Li, R, Zhang, C, Xue, X & Wu, X 1970, 'Cross-domain collaborative filtering over time', IJCAI International Joint Conference on Artificial Intelligence, International Joint Conference on Artificial Intelligence, AAAI Press, Barcelona, Catalonia, Spain, pp. 2293-2298.
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Collaborative filtering (CF) techniques recommend items to users based on their historical ratings. In real-world scenarios, user interests may drift over time since they are affected by moods, contexts, and pop culture trends. This leads to the fact that a user's historical ratings comprise many aspects of user interests spanning a long time period. However, at a certain time slice, one user's interest may only focus on one or a couple of aspects. Thus, CF techniques based on the entire historical ratings may recommend inappropriate items. In this paper, we consider modeling user-interest drift over time based on the assumption that each user has multiple counterparts over temporal domains and successive counterparts are closely related. We adopt the cross-domain CF framework to share the static group-level rating matrix across temporal domains, and let user-interest distribution over item groups drift slightly between successive temporal domains. The derived method is based on a Bayesian latent factor model which can be inferred using Gibbs sampling. Our experimental results show that our method can achieve state-of-the-art recommendation performance as well as explicitly track and visualize user-interest drift over time.
Li, L, Xu, G, Yang, Z, Zhang, Y & Kitsuregawa, M 1970, 'A Feature-Free Flexible Approach to Topical Classification of Web Queries', 2011 Seventh International Conference on Semantics, Knowledge and Grids, 2011 Seventh International Conference on Semantics Knowledge and Grid (SKG), IEEE, Beijing, China, pp. 59-66.
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The task of topical classification of Web queries is to classify Web queries into a set of target categories. Machine learning based conventional approaches usually rely on external sources of information to obtain additional features for Web queries and training data for target categories. Unfortunately, these approaches are known to suffer from inability to adapt to different target categories which may be caused by the dynamic changes observed in both Web topic taxonomy and Web content. In this paper, we propose a feature-free flexible approach to topical classification of Web queries. Our approach analyzes queries and topical categories themselves and utilizes the number of Web pages containing both a query and a category to determine their similarity. The most attractive feature of our approach is that it only utilizes the Web page counts estimated by a search engine to provide the Web query classification with respectable accuracy. We conduct experimental study on the effectiveness of our approach using a set of rank measures and show that our approach performs competitively to some popular state-of-the-art solutions which, however, make frequent use of external sources and are inherently insufficient in flexibility. © 2011 IEEE.
Li, Z, Wu, Q, Zhang, J & Geers, G 1970, 'SKRWM based descriptor for pedestrian detection in thermal images', 2011 IEEE 13th International Workshop on Multimedia Signal Processing, 2011 IEEE 13th International Workshop on Multimedia Signal Processing (MMSP), IEEE, Hangzhou, China, pp. 1-6.
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Pedestrian detection in a thermal image is a difficult task due to intrinsic challenges:1) low image resolution, 2) thermal noising, 3) polarity changes, 4) lack of color, texture or depth information. To address these challenges, we propose a novel mid-level feature descriptor for pedestrian detection in thermal domain, which combines pixel-level Steering Kernel Regression Weights Matrix (SKRWM) with their corresponding covariances. SKRWM can properly capture the local structure of pixels, while the covariance computation can further provide the correlation of low level feature. This mid-level feature descriptor not only captures the pixel-level data difference and spatial differences of local structure, but also explores the correlations among low-level features. In the case of human detection, the proposed mid-level feature descriptor can discriminatively distinguish pedestrian from complexity. For testing the performance of proposed feature descriptor, a popular classifier framework based on Principal Component Analysis (PCA) and Support Vector Machine (SVM) is also built. Overall, our experimental results show that proposed approach has overcome the problems caused by background subtraction in [1] while attains comparable detection accuracy compared to the state-of-the-arts. © 2011 IEEE.
Liang, G & Zhang, C 1970, 'An Empirical Evaluation of Bagging with Different Algorithms on Imbalanced Data', Advanced Data Mining and Applications. Lecture Notes in Artificial Intelligence 7120, International Conference on Advanced Data Mining and Applications, Springer Berlin Heidelberg, Beijing, China, pp. 339-352.
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This study investigates the effectiveness of bagging with respect to different learning algorithms on Imbalanced data-sets. The purpose of this research is to investigate the performance of bagging based on two unique approaches: (1) classify base learners with respect to 12 different learning algorithms in general terms, and (2) evaluate the performance of bagging predictors on data with imbalanced class distributions. The former approach develops a method to categorize base learners by using two-dimensional robustness and stability decomposition on 48 benchmark data-sets; while the latter approach investigates the performance of bagging predictors by using evaluation metrics, True Positive Rate (TPR), Geometric mean (G-mean) for the accuracy on the majority and minority classes, and the Receiver Operating Characteristic (ROC) curve on 12 imbalanced data-sets. Our studies assert that both stability and robustness are important factors for building high performance bagging predictors on data with imbalanced class distributions. The experimental results demonstrated that PART and Multi-layer Proceptron (MLP) are the learning algorithms with the best bagging performance on 12 imbalanced data-sets. Moreover, only four out of 12 bagging predictors are statistically superior to single learners based on both G-mean and TPR evaluation metrics over 12 imbalanced data-sets.
Liang, G, Zhu, X & Zhang, C 1970, 'An empirical study of bagging predictors for different learning algorithms', Proceedings of the National Conference on Artificial Intelligence, National Conference of the American Association for Artificial Intelligence, AAAI Press, San Francisco, California, US, pp. 1802-1803.
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Bagging is a simple, yet effective design which combines multiple base learners to form an ensemble for prediction. Despite its popular usage in many real-world applications, existing research is mainly concerned with studying unstable learners as the key to ensure the performance gain of a bagging predictor, with many key factors remaining unclear. For example, it is not clear when a bagging predictor can outperform a single learner and what is the expected performance gain when different learning algorithms were used to form a bagging predictor. In this paper, we carry out comprehensive empirical studies to evaluate bagging predictors by using 12 different learning algorithms and 48 benchmark data-sets. Our analysis uses robustness and stability decompositions to characterize different learning algorithms, through which we rank all learning algorithms and comparatively study their bagging predictors to draw conclusions. Our studies assert that both stability and robustness are key requirements to ensure the high performance for building a bagging predictor. In addition, our studies demonstrated that bagging is statistically superior to most single learners, except for KNN and Naïve Bayes (NB). Multi-layer perception (MLP), Naïve Bayes Trees (NBTree), and PART are the learning algorithms with the best bagging performance. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.
Liang, G, Zhu, X & Zhang, C 1970, 'An Empirical Study of Bagging Predictors for Different Learning Algorithms', Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011, pp. 1802-1803.
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Bagging is a simple, yet effective design which combines multiple base learners to form an ensemble for prediction. Despite its popular usage in many real-world applications, existing research is mainly concerned with studying unstable learners as the key to ensure the performance gain of a bagging predictor, with many key factors remaining unclear. For example, it is not clear when a bagging predictor can outperform a single learner and what is the expected performance gain when different learning algorithms were used to form a bagging predictor. In this paper, we carry out comprehensive empirical studies to evaluate bagging predictors by using 12 different learning algorithms and 48 benchmark data-sets. Our analysis uses robustness and stability decompositions to characterize different learning algorithms, through which we rank all learning algorithms and comparatively study their bagging predictors to draw conclusions. Our studies assert that both stability and robustness are key requirements to ensure the high performance for building a bagging predictor. In addition, our studies demonstrated that bagging is statistically superior to most single learners, except for KNN and Naïve Bayes (NB). Multi-layer perception (MLP), Naïve Bayes Trees (NBTree), and PART are the learning algorithms with the best bagging performance.
Liang, G, Zhu, X & Zhang, C 1970, 'An Empirical Study of Bagging Predictors for Imbalanced Data with Different Levels of Class Distribution', AI 2011: Advances in Artificial Intelligence, Australasian Joint Conference on Artificial Intelligence, Springer Berlin Heidelberg, Perth, Australia, pp. 213-222.
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Research into learning from imbalanced data has increasingly captured the attention of both academia and industry, especially when the class distribution is highly skewed. This paper compares the Area Under the Receiver Operating Characteristic Curve (AUC) performance of bagging in the context of learning from different imbalanced levels of class distribution. Despite the popularity of bagging in many real-world applications, some questions have not been clearly answered in the existing research, e.g., which bagging predictors may achieve the best performance for applications, and whether bagging is superior to single learners when the levels of class distribution change. We perform a comprehensive evaluation of the AUC performance of bagging predictors with 12 base learners at different imbalanced levels of class distribution by using a sampling technique on 14 imbalanced data-sets. Our experimental results indicate that Decision Table (DTable) and RepTree are the learning algorithms with the best bagging AUC performance. Most AUC performances of bagging predictors are statistically superior to single learners, except for Support Vector Machines (SVM) and Decision Stump (DStump).
Liu, X & Zhang, J 1970, 'Active learning for human action recognition with Gaussian Processes', 2011 18th IEEE International Conference on Image Processing, 2011 18th IEEE International Conference on Image Processing (ICIP 2011), IEEE, Brussels, Belgium, pp. 3253-3256.
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This paper presents an active learning approach for recognizing human actions in videos based on multiple kernel combined method. We design the classifier based on Multiple Kernel Learning (MKL) through Gaussian Processes (GP) regression. This classifier is then trained in an active learning approach. In each iteration, one optimal sample is selected to be interactively annotated and incorporated into training set. The selection of the sample is based on the heuristic feedback of the GP classifier. To our knowledge, GP regression MKL based active learning methods have not been applied to address the human action recognition yet. We test this approach on standard benchmarks. This approach outperforms the state-of-the-art techniques in accuracy while requires significantly less training samples. © 2011 IEEE.
Luo, C, Zhao, Y, Luo, D, Zhang, C & Cao, W 1970, 'Agent-Based Subspace Clustering', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Berlin Heidelberg, pp. 370-381.
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This paper presents an agent-based algorithm for discovering subspace clusters in high dimensional data. Each data object is represented by an agent, and the agents move from one local environment to another to find optimal clusters in subspaces. Heuristic rules and objective functions are defined to guide the movements of agents, so that similar agents(data objects) go to one group. The experimental results show that our proposed agent-based subspace clustering algorithm performs better than existing subspace clustering methods on both F1 measure and Entropy. The running time of our algorithm is scalable with the size and dimensionality of data. Furthermore, an application in stock market surveillance demonstrates its effectiveness in real world applications. © 2011 Springer-Verlag.
Mao, Y, Cui, K, Lulu, W, Zhao, H, Nie, F, Brandl, M, Beck, D, Gao, L & Wong, S 1970, 'An in-silico approach for drug repositioning to tumour anti-migration using an integrated genomic strategy', 2011 IEEE/NIH Life Science Systems and Applications Workshop (LiSSA), 2011 IEEE/NIH 5th Life Science Systems and Applications Workshop (LiSSA), IEEE, Bethesda, MD, USA, pp. 88-91.
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Cell migration is a key step for deterioration of many in situ or metastasis malignant tumours. Tumour anti-migration is a promising strategy to treat cancer, but corresponding drugs developed under such a strategy are still in dire poverty, partly due to the lengthly process of drug trials and approval required by the US Food and Drug Administration (FDA). Given there are thousands of FDA approved drugs in the market, we believe that drug repositioning may provide a fast and cost-effective way to identify potential anti-migration drugs. In this paper, an in-silico drug screening method using a genomic strategy is proposed for the goal, in which genomic signature identification combined with support vector machine modelling is adopted to estimate drug efficacy. And a high-throughput, sensitive, 3-dimensional invasion assay by quantitative bioluminescence imaging proved the performance of proposed method on in vitro disease models. © 2011 IEEE.
Marin, L, Merigo, JM, Valls, A, Moreno, A & Isern, D 1970, 'Induced Unbalanced Linguistic Ordered Weighted Average', Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011), 7th conference of the European Society for Fuzzy Logic and Technology, Atlantis Press, Aix-les-Bains, FRANCE, pp. 1-8.
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Merigo, JM 1970, 'A Unified Model for Fuzzy Aggregation Operators and its Application in Group Decision Making', Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011), 7th conference of the European Society for Fuzzy Logic and Technology, Atlantis Press, Aix-les-Bains, FRANCE, pp. 965-972.
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Merigo, JM 1970, 'Decision making with probabilities, weighted averages and OWA operators', 2011 IEEE Symposium on Foundations of Computational Intelligence (FOCI), 2011 Ieee Symposium On Foundations Of Computational Intelligence - Part Of 17273 - 2011 Sscifo, IEEE, pp. 122-129.
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We develop a new method for decision making based on the use of probabilities, weighted averages and ordered weighted averaging (OWA) operators. We analyze a method that it is able to deal with several aggregation structures thus obtaining a more general formulation that represents the information in a more complete way. We introduce a new aggregation operator that aggregates a wide range of other aggregation operators. Therefore, we can include in the same formulation a wide range of concepts and representing how relevant they are in the aggregation. We call it the unified aggregation operator. By using this aggregation operator we can deal with a wide range of complex structures, for example, we can aggregate in a decision making problem several structures of probabilities, weighted averages and OWA operators. Thus, the information we provide is more complete because in real world problems the information comes from different sources and this needs to be considered in the aggregation process. We study the applicability of this new approach and we see that it is very broad because real world problems are better assessed with this new model. We focus on a multi-person decision making example where we use several structures of probabilities, weighted averages and OWA operators, thus representing the subjective and the objective information and the attitudinal character in a more complete way. © 2011 IEEE.
Merigo, JM & Casanovas, M 1970, 'FUZZY GROUP DECISION MAKING IN RESEARCH MANAGEMENT', EDULEARN11: 3RD INTERNATIONAL CONFERENCE ON EDUCATION AND NEW LEARNING TECHNOLOGIES, 3rd International Conference on Education and New Learning Technologies (EDULEARN), IATED-INT ASSOC TECHNOLOGY EDUCATION & DEVELOPMENT, Barcelona, SPAIN, pp. 6057-6064.
Merigo, JM & Casanovas, M 1970, 'The Uncertain Generalized Probabilistic Weighted Average and its Application in the Theory of Expertons', Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011), 7th conference of the European Society for Fuzzy Logic and Technology, Atlantis Press, Aix-les-Bains, FRANCE, pp. 852-859.
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Merigo, JM & Gil-Lafuente, AM 1970, 'Financial decision making with distance measures and induced probabilistic generalized aggregation operators', 2011 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr), Economics -Part Of 17273 - 2011 Ssci, IEEE, pp. 116-123.
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We develop a new financial decision making approach by using induced and probabilistic generalized aggregation operators. We introduce the induced generalized probabilistic ordered weighted averaging distance (IGPOWAD) operator and some of its main properties. Its main advantage is that it uses distance measures in a unified framework between the probability and the OWA operator where we can consider the degree of importance of each concept in the aggregation. Moreover, it also uses order-inducing variables that represent complex reordering processes in the aggregation. We develop an application of this new approach in a financial multi-person decision making problem regarding the selection of financial strategies. We see that the opinion of several experts provides more robust information for the decision maker. © 2011 IEEE.
Merigo, JM, Lopez-Jurado, P & Carmen Gracia, M 1970, 'MAKING DECISIONS IN EDUCATIONAL MANAGEMENT WITH PROBABILISTIC AND IMPRECISE INFORMATION', EDULEARN11: 3RD INTERNATIONAL CONFERENCE ON EDUCATION AND NEW LEARNING TECHNOLOGIES, 3rd International Conference on Education and New Learning Technologies (EDULEARN), IATED-INT ASSOC TECHNOLOGY EDUCATION & DEVELOPMENT, Barcelona, SPAIN, pp. 6041-6051.
Moghaddam, Z & Piccardi, M 1970, 'Robust density modelling using the student's t-distribution for human action recognition', 2011 18th IEEE International Conference on Image Processing, 2011 18th IEEE International Conference on Image Processing (ICIP 2011), IEEE, Brussels Belgium, pp. 3261-3264.
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The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy.
Movassaghi, S, Abolhasan, M & Lipman, J 1970, 'Hierarchical Collision-free Addressing Protocol(HCAP) for Body Area Networks', 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE INFOCOM 2011 - IEEE Conference on Computer Communications Workshops, IEEE, Shanghai, China, pp. 543-548.
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In Body Area Networks (BANs) the addressing scheme used to address nodes is fundamental to the effective operation of a BAN. This paper proposes a novel BAN addressing scheme called Hierarchical Collision-free Addressing Protocol (HCAP). Proposed scheme is collision free, reduces power consumption and tackles the address wastage problem. Two important scenarios (random location and fixed location) are defined and studied. Through a series of simulation results we show the efficiency and usability of the proposed scheme in Body Area Networks. © 2011 IEEE.
Movassaghi, S, Abolhasan, M, Lipman, J & IEEE 1970, 'Optimized Prophet Address Allocation (OPAA) for Body Area Networks', 2011 7TH INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING CONFERENCE (IWCMC), ACM International Wireless Communications and Mobile Computing Conference, IEEE, Istanbul, Turkey, pp. 2098-2102.
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Each node in a Body Area Network (BAN) needs to be assigned with a free IP address before it may participate in any sort of communication. This paper evaluates the performance of an IP address allocation scheme, namely Prophet allocation to be used for BANs. This allocation scheme is a fully decentralized addressing scheme which is applicable to BANs as it provides low latency, low communication overhead and low complexity. Relative theoretical analysis and simulation experiments have also been conducted to demonstrate its benefits which also represent the reason for the choice of this allocation scheme. It also solves the issues related to network partition and merger efficiently. © 2011 IEEE.
Otoom, AF, Concha, OP & Piccardi, M 1970, 'Boosting mixtures of gaussians under normalized linear transformations for image classification', 7th International Conference on Information Technology and Application, ICITA 2011, International Conference on Information Technology and Applications, IEEE, Sydney Australia, pp. 184-189.
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We address the problem of image classification. Our aim is to improve the performance of MLiT: mixture of Gaussians under Linear transformations, a feature-based classifier proposed in [1] aiming to reduce dimensionality based on a linear transformation which is not restricted to be orthogonal. Boosting might offer an interesting solution by improving the performance of a given base classification algorithm. In this paper, we propose to integrate MLiT within the framework of AdaBoost, which is a widely applied method for boosting. For experimental validation, we have evaluated the proposed method on the four UCI data sets (Vehicle, OpticDigit, WDBC, WPBC) [2] and the author's own. Boosting has proved capable of enhancing the performance of the base classifier on two data sets with improvements of up to 12.8%.
Papapetrou, O & Chen, L 1970, 'XStreamCluster: An Efficient Algorithm for Streaming XML Data Clustering', Lecture Notes in Computer Science: Database Systems for Advanced Applications 16th International Conference, DASFAA 2011, Database Systems for Advanced Applications, Springer Berlin Heidelberg, Hongkong, China, pp. 496-510.
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XML clustering finds many applications, ranging from storage to query processing. However, existing clustering algorithms focus on static XML collections, whereas modern information systems frequently deal with streaming XML data that needs to be processed online. Streaming XML clustering is a challenging task because of the high computational and space efficiency requirements implicated for online approaches. In this paper we propose XStreamCluster, which addresses the two challenges using a two-layered optimization. The bottom layer employs Bloom filters to encode the XML documents, providing a space-efficient solution to memory usage. The top layer is based on Locality Sensitive Hashing and contributes to the computational efficiency. The theoretical analysis shows that the approximate solution of XStreamCluster generates similarly good clusters as the exact solution, with high probability. The experimental results demonstrate that XStreamCluster improves both memory efficiency and computational time by at least an order of magnitude without affecting clustering quality, compared to its variants and a baseline approach.
Parvin, S & Hussain, FK 1970, 'Digital Signature-Based Secure Communication in Cognitive Radio Networks', 2011 International Conference on Broadband and Wireless Computing, Communication and Applications, 2011 International Conference on Broadband, Wireless Computing, Communication and Applications (BWCCA), IEEE, Barcelona, Spain, pp. 230-235.
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Due to the rapid growth of wireless applications, Cognitive Radio (CR) has been considered as a demanding concept for improving the utilization of limited radio spectrum resources for future wireless communications and mobile computing. The unique characteristics of Cognitive Radio Networks (CRNs) make security more challenging. Since a member of Cognitive Radio Networks may join or leave the network at any time, the issue of supporting secure communication in CRNs becomes more critical than for other conventional wireless networks. This work thus proposes digital signature-based secure communication for identifying efficient primary users in CRNs. The security analysis is analyzed to guarantee that the proposed approach achieves security proof. © 2011 IEEE.
Quek, A, Wang, Z, Zhang, J & Feng, D 1970, 'Structural Image Classification with Graph Neural Networks', 2011 International Conference on Digital Image Computing: Techniques and Applications, 2011 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE, Noosa, Queensland, Australia, pp. 416-421.
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Many approaches to image classification tend to transform an image into an unstructured set of numeric feature vectors obtained globally and/or locally, and as a result lose important relational information between regions. In order to encode the geometric relationships between image regions, we propose a variety of structural image representations that are not specialised for any particular image category. Besides the traditional grid-partitioning and global segmentation methods, we investigate the use of local scale-invariant region detectors. Regions are connected based not only upon nearest-neighbour heuristics, but also upon minimum spanning trees and Delaunay triangulation. In order to maintain the topological and spatial relationships between regions, and also to effectively process undirected connections represented as graphs, we utilise the recently-proposed graph neural network model. To the best of our knowledge, this is the first utilisation of the model to process graph structures based on local-sampling techniques, for the task of image classification. Our experimental results demonstrate great potential for further work in this domain. © 2011 IEEE.
Rehman, ZU, Hussain, FK & Hussain, OK 1970, 'Towards Multi-criteria Cloud Service Selection', 2011 Fifth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, 2011 Fifth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), IEEE, Seoul, South Korea, pp. 44-48.
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Cloud computing despite being in an early stage of adoption is becoming a popular choice for businesses to replace in-house IT infrastructure due to its technological advantages such as elastic computing and cost benefits resulting from pay-as-you-go pricing and economy of scale. These factors have led to a rapid increase in both the number of cloud vendors and services on offer. Given that cloud services could be characterized using multiple criteria (cost, pricing policy, performance etc.) it is important to have a methodology for selecting cloud services based on multiple criteria. Additionally, the end user requirements might map to different criteria of the cloud services. This diversity in services and the number of available options have complicated the process of service and vendor selection for prospective cloud users and there is a need for a comprehensive methodology for cloud service selection. The existing research literature in cloud service selection is mostly concerned with comparison between similar services based on cost or performance benchmarks. In this paper we discuss and formalize the issue of cloud service selection in general and propose a multi-criteria cloud service selection methodology. © 2011 IEEE.
Rudduck, SG, Williams, MA & Stoianoff, N 1970, 'Visualizing the shape of quality: An application in the context of intellectual property', CEUR Workshop Proceedings, Interdisciplinary Workshop on SHAPES, CUER Wokshop Proceeedings, Karlsruhe, Germany, pp. 1-10.
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The aim of this work is to explore how the concept of shape can be applied in the context of Intellectual Property Law (IPL). Despite the global nature of IPL, the system is plagued with considerable uncertainty, especially in the specific instrument of patents. We believe the shape concept can find a balance between the inventive ideas, patent claims and objects in the world. The outcomes of this can then be measured as a time-dependent expectancy that an invention will conform to legal rules when under examination by officials. Specifically, we establish an empirical-based benchmark which can be utilized to test whether shape (via visual figures) is useful in reducing the uncertainty (measured via number of examination actions) which an applicant might face in patenting technological ideas.
Santosa, H, Milton, J & Kennedy, PJ 1970, 'HMXT-GP', Proceedings of the 2011 ACM Symposium on Applied Computing, SAC'11: The 2011 ACM Symposium on Applied Computing, ACM, Taichung, Taiwan, pp. 1070-1075.
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This paper applies a recent informationtheoretic approach to controlling Genetic Algorithms (GAs) called HMXT to treebased Genetic Programming (GP). HMXT, in a GA domain, requires the setting of selection thresholds in a population and the application of high levels of crossover to thoroughly mix alleles. Applying these in a treebased GP setting is not trivial. We present results comparing HMXT GP to Kozastyle GP for varying amounts of crossover and over three different optimisation (minimisation) problems. Results show that average fitness is better with HMXTGP because it maintains more diversity in populations, but that the minimum fitness found was better with Koza. HMXT allows straightforward tuning of population diversity and selection pressure by altering the position of the selection thresholds.
Vamplew, P, Stranieri, A, Ong, K, Christen, P & Kennedy, PJ 1970, 'Data Mining and Analytics 2011 (AusDM'11)', Proceedings of the Ninth Australasian Data Mining Conference (AusDM'11), Ninth Australasian Data Mining Conference, Australian Computer Society, Ballarat, Australia, pp. i-229.
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We are delighted to welcome you to the Ninth Australasian Data Mining Conference (AusDM'11) being held this year in Ballarat, Victoria. AusDM started in 2002 and is now the annual flagship meeting for data mining and analytics professionals in Australia. Both scholars and practitioners present the state-of-the-art in the field. Endorsed by the peak professional body, the Institute of Analytics Professionals of Australia, AusDM has developed a unique profile in nurturing this joint community. The conference series has grown in size each year from early events held in Canberra (2002, 2003), Cairns (2004), Sydney (2005, 2006), the Gold Coast (2007), Glenelg (2008) and Melbourne (2009).
Van, A, Gay, V, Kennedy, PJ, Barin, E & Leijdekkers, P 1970, 'Understanding risk factors in cardiac rehabilitation patients with random forests and decision trees.', AusDM, Australian Data Mining Conference, Australian Computer Society, Ballarat, Australia, pp. 11-22.
Wang, C, Cao, L, Wang, M, Li, J, Wei, W & Ou, Y 1970, 'Coupled nominal similarity in unsupervised learning', Proceedings of the 20th ACM international conference on Information and knowledge management, CIKM '11: International Conference on Information and Knowledge Management, ACM, Glasgow, UK, pp. 973-978.
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The similarity between nominal objects is not straightforward, especially in unsupervised learning. This paper proposes coupled similarity metrics for nominal objects, which consider not only intra-coupled similarity within an attribute (i.e., value frequency distribution) but also inter-coupled similarity between attributes (i.e. feature dependency aggregation). Four metrics are designed to calculate the inter-coupled similarity between two categorical values by considering their relationships with other attributes. The theoretical analysis reveals their equivalent accuracy and superior efficiency based on intersection against others, in particular for large-scale data. Substantial experiments on extensive UCI data sets verify the theoretical conclusions. In addition, experiments of clustering based on the derived dissimilarity metrics show a significant performance improvement. © 2011 ACM.
Wang, L, He, X, Du, R, Jia, W, Wu, Q & Yeh, W-C 1970, 'Facial Expression Recognition on Hexagonal Structure Using LBP-Based Histogram Variances', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Multimedia Modeling Conference, Springer Berlin Heidelberg, Taipei, Taiwan, pp. 35-45.
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In our earlier work, we have proposed an HVF (Histogram Variance Face) approach and proved its effectiveness for facial expression recognition. In this paper, we extend the HVF approach and present a novel approach for facial expression. We take into account the human perspective and understanding of facial expressions. For the first time, we propose to use the Local Binary Pattern (LBP) defined on the hexagonal structure to extract local, dynamic facial features from facial expression images. The dynamic LBP features are used to construct a static image, namely Hexagonal Histogram Variance Face (HHVF), for the video representing a facial expression. We show that the HHVFs representing the same facial expression (e.g., surprise, happy and sadness etc.) are similar no matter if the performers and frame rates are different. Therefore, the proposed facial recognition approach can be utilised for the dynamic expression recognition. We have tested our approach on the well-known Cohn-Kanade AU-Coded Facial Expression database. We have found the improved accuracy of HHVF-based classification compared with the HVF-based approach. © 2011 Springer-Verlag Berlin Heidelberg.
Wang, S, Jia, W, Wu, Q, He, X & Yang, J 1970, 'Learning Global and Local Features for License Plate Detection', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Neural Information Processing, Springer Berlin Heidelberg, Shanghai, China, pp. 547-556.
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This paper proposes an intelligent system that is capable of automatically detecting license plates from static images captured by a digital still camera. A supervised learning approach is used to extract features from license plates, and both global feature and local feature are organized into a cascaded structure. In general, our framework can be divided into two stages. The first stage is constructed by extracting global correlation features and a posterior probability can be estimated to quickly determine the degree of resemblance between the evaluated image region and a license plate. The second stage is constructed by further extracting local dense-SIFT (dSIFT) features for AdaBoost supervised learning approach, and the selected dSIFT features will be used to construct a strong classifier. Using dSIFT as a type of highly distinctive local feature, our algorithm gives high detection rate under various complex conditions. The proposed framework is compared with existing works and promising results are obtained. © 2011 Springer-Verlag.
Wang, S, Wu, Q, He, X & Jia, W 1970, 'More on Weak Feature: Self-correlate Histogram Distances', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific-Rim Symposium on Image and Video Technology, Springer Berlin Heidelberg, Gwangju, South Korea, pp. 214-223.
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In object detection research, there is a discussion on weak feature and strong feature, feature descriptors, regardless of being considered as 'weak feature descriptors' or 'strong feature descriptors' does not necessarily imply detector performance unless combined with relevant classification algorithms. Since 2001, main stream object detection research projects have been following the Viola Jone's weak feature (Haar-like feature) and AdaBoost classifier approach. Until 2005, when Dalal and Triggs have created the approach of a strong feature (Histogram of Oriented Gradient) and Support Vector Machine (SVM) framework for human detection. This paper proposes an approach to improve the salience of a weak feature descriptor by using intra-feature correlation. Although the intensity histogram distance feature known as Histogram Distance of Haar Regions (HDHR) itself is considered as a weak feature and can only be used to construct a weak learner to learn an AdaBoost classifier. In our paper, we explore the pairwise correlations between each and every histograms constructed and a strong feature can then be formulated. With the newly constructed strong feature based on histogram distances, a SVM classifier can be trained and later used for classification tasks. Promising experimental results have been obtained. © 2011 Springer-Verlag.
Wang, W, Wu, Q, Jia, W & He, S 1970, 'Training-Free License Plate Detection Using Vehicle Symmetry and Simple Features', Proceedings: Twenty-sixth International Conference Image and Vision Computing New Zealand, Image and Vision Computing New Zealand (IVCNZ), Image and Vision Computing New Zealand, Auckland, New Zealand, pp. 260-265.
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In this paper, we propose a training free license plate detection method. We use a challenging benchmark dataset for license plate detection. Unlike many existing approaches, the proposed approach is a training free method, which does not require supervised training procedure and yet can achieve a reasonably good performance. Our motivation comes from the fact that, although license plates are largely variant in color, size, aspect ratio, illumination condition and so on, the rear view of vehicles is mostly symmetric with regard to the vehicles central axis. In addition, license plates for most vehicles are usually located on or close to the vertical axis of the vehicle body along which the vehicle is nearly symmetric. Taking advantage of such prior knowledge, the license plate detection problem is made simpler compared to the conventional scanning window approach which not only requires a large number of scanning window locations, but also requires different parameter settings such as scanning window sizes, aspect ratios and so on.
Wu, Z, Xu, G, Pan, R, Zhang, Y, Hu, Z & Lu, J 1970, 'Leveraging Wikipedia concept and category information to enhance contextual advertising', Proceedings of the 20th ACM international conference on Information and knowledge management, CIKM '11: International Conference on Information and Knowledge Management, ACM, Glasgow, Scotland, UK, pp. 2105-2108.
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Xu, G, Gu, Y, Dolog, P, Zhang, Y & Kitsuregawa, M 1970, 'SemRec: A semantic enhancement framework for tag based recommendation', Proceedings of the National Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, AAAI Press, San Francisco, California, pp. 1267-1272.
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Collaborative tagging services provided by various social web sites become popular means to mark web resources for different purposes such as categorization, expression of a preference and so on. However, the tags are of syntactic nature, in a free style and do not reflect semantics, resulting in the problems of redundancy, ambiguity and less semantics. Current tag-based recommender systems mainly take the explicit structural information among users, resources and tags into consideration, while neglecting the important implicit semantic relationships hidden in tagging data. In this study, we propose a Semantic Enhancement Recommendation strategy (SemRec), based on both structural information and semantic information through a unified fusion model. Extensive experiments conducted on two real datasets demonstrate the effectiveness of our approaches. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.
Xu, G, Gu, Y, Zhang, Y, Yang, Z & Kitsuregawa, M 1970, 'TOAST: A Topic-Oriented Tag-Based Recommender System', Web Information Systems Engineering - Wise 2011, 12th International Conference on Web Information Systems Engineering (WISE 2011), Springer Berlin Heidelberg, Sydney, AUSTRALIA, pp. 158-171.
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Social Annotation Systems have emerged as a popular application with the advance of Web 2.0 technologies. Tags generated by users using arbitrary words to express their own opinions and perceptions on various resources provide a new intermediate dimensio
Xu, G, Lee, W, Chen, L & Chen, L 1970, 'Message from CSN 2011 Workshop Co-chairs', 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing, 2011 IEEE 9th International Conference on Dependable, Autonomic and Secure Computing (DASC), IEEE.
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Xu, G, Zong, Y, Pan, R, Dolog, P & Jin, P 1970, 'On Kernel Information Propagation for Tag Clustering in Social Annotation Systems', Knowlege-Based and Intelligent Information and Engineering Systems Lecture Notes in Computer Science, International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, Springer Berlin Heidelberg, Kaiserslautern, Germany, pp. 505-514.
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In social annotation systems, users label digital resources by using tags which are freely chosen textual descriptors. Tags are used to index, annotate and retrieve resource as an additional metadata of re- source. Poor retrieval performance remains a major challenge of most social annotation systems resulting from the severe problems of ambigu- ity, redundancy and less semantic nature of tags. Clustering method is a useful approach to handle these problems in the social annotation sys- tems. In this paper, we propose a novel clustering algorithm named kernel information propagation for tag clustering. This approach makes use of the kernel density estimation of the KNN neighbor directed graph as a start to reveal the prestige rank of tags in tagging data. The random walk with restart algorithm is then employed to determine the center points of tag clusters. The main strength of the proposed approach is the capability of partitioning tags from the perspective of tag prestige rank rather than the intuitive similarity calculation itself. Experimental studies on three real world datasets demonstrate the effectiveness and superiority of the proposed method.
Yu, JX, Lei Chen, Sakr, S & Lei Zou 1970, 'Preface', 2011 IEEE 27th International Conference on Data Engineering Workshops, 2011 IEEE International Conference on Data Engineering Workshops (ICDEW), IEEE, p. 87.
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Recently, there has been a lot of interest in the application of graphs in different domains. They have been widely used for data modeling of different application domains such as chemical compounds, multimedia databases, protein networks, social networks and semantic web. With the continued emergence and increase of massive and complex structural graph data, a graph database that efficiently supports elementary data management mechanisms is crucially required to effectively understand and utilize any collection of graphs. This workshop focuses on issues related to graph databases. GDM 2011 will be held in conjunction with the IEEE International Conference on Data Engineering (ICDE 2011) in Hannover, Germany. GDM 2011 aims at bringing together researchers in different fields related to graph databases who have common interests in interdisciplinary research. The workshop provides a forum where researchers and practitioners can share and exchange their knowledge and experience. © 2011 IEEE.
Yu, PS, Fan, W, Nejdl, W, Chen, L, Sun, A, Simovici, D, Baralis, E, Nguifo, EM, Xu, G, Yin, J, Ceci, M, Cortez, P, Christen, P, Berka, P, Alves, R, Xu, S, Elomaa, T, Kosters, W, Graco, W, Wang, W, Balke, WT & Zhao, Y 1970, 'Preface to the Workshop on Domain Driven Data Mining', 2011 IEEE 11th International Conference on Data Mining Workshops, 2011 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE.
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Zhu, L, Cao, L & Yang, J 1970, 'Soft subspace clustering with competitive agglomeration', 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Taipei, pp. 691-698.
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In this paper, two novel soft subspace clustering algorithms, namely fuzzy weighting subspace clustering with competitive agglomeration (FWSCA) and entropy weighting subspace clustering with competitive agglomeration (EWSCA), are proposed to overcome the problems of the unknown number of clusters and the initialization of prototypes for soft subspace clustering. The main advantage of FWSCA and EWSCA lies in the fact that they effectively integrate the merits of soft subspace clustering and the good properties of fuzzy clustering with competitive agglomeration. This makes it possible to obtain the appropriate number of clusters during the clustering progress. Moreover, FWSCA and EWSCA algorithms can converge regardless of the initial number of clusters and initialization. Substantial experimental results on both synthetic and real data sets demonstrate the effectiveness of FWSCA and EWSCA in addressing the two problems
Zhu, ZY, Li, JY, Wang, LN, Liu, W, Cui, M & Liu, L 1970, 'A Simplified Time-Division Based on Road Network Model Considering Intersection Delay for Vehicle Navigation', Applied Mechanics and Materials, 1st International Conference on Mechanical Engineering, Trans Tech Publications, Ltd., THAILAND, Phuket, pp. 1226-1232.
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How to model a dynamic road network has great practical significance in a vehicle navigation system. This paper has proposed a simplified time-division based on road network model which implicitly takes into account the delay time at various intersections, the degree of a road congestion and the different road quality, but avoids a complicated calculation and collection for these traffic data. An improved Dijkstra algorithm based on the new model has also been given. The simulation results show that the model can work well and the algorithm is efficient.
Zhu, ZY, Liu, W, Liu, L, Cui, M & Li, JY 1970, 'A Simplified Real-Time Road Network Model Considering Intersection Delay and its Application on Vehicle Navigation', Applied Mechanics and Materials, International Conference on Information Technology for Manufacturing Systems (ITMS 2011), Trans Tech Publications, Ltd., PEOPLES R CHINA, Shanghai, pp. 1959-1965.
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The complexity of a real road network structure of a city and the variability of its real traffic information make a city’s intelligent transportation system (ITS) hard to meet the needs of the city’s vehicle navigation. This paper has proposed a simplified real-time road network model which can take into account the influence of intersection delay on the guidance for vehicles but avoid the calculation of intersection delay and troublesome collection of a city’s traffic data. Based on the new model, a navigation system has been presented, which can plan a dynamic optimal path for a vehicle according to the real-time traffic data received periodically from the city’s traffic center. A simulated experiment has been given. Compared with previous real-time road network models, the new model is much simpler and more effective on the calculation of vehicle navigation.
Zong, Y, Xu, G, Jin, P, Dolog, P & Jiang, S 1970, 'A Local Information Passing Clustering Algorithm for Tagging Systems', Database Systems for Adanced Applications, Lecture Notes in Computer Science, Database Systems for Advanced Applications, Springer Berlin Heidelberg, Hong Kong, China, pp. 333-343.
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Under social tagging systems, a typical Web2.0 application, users label digital data sources by using tags which are freely chosen textual descriptions. Tags are used to index, annotate and retrieve resource as an additional metadata of resource. Poor retrieval performance remains a major problem of most social tagging systems resulting from the severe difficulty of ambiguity, redundancy and less semantic nature of tags. Clustering method is a useful tool to increase the ability of information retrieval in the aforementioned systems. In this paper, we propose a novel clustering algorithm named LIPC (Local Information Passing Clustering algorithm). The main steps of LIPC are: (1) we estimate a KNN neighbor directed graph G of tags and calculate the kernel density of each tag in its neighborhood; (2) we generate local information, local coverage and local kernel of each tag; (3) we pass the local information on G by I and O operators until they are converged and tag priory are generated; (4) we use tag priory to find out the clusters of tags. Experimental results on two real world datasets namely MedWorm and MovieLens demonstrate the efficiency and the superiority of the proposed method.
Zong, Y, Xu, G, Jin, P, Zhang, Y, Chen, E & Pan, R 1970, 'APPECT: An Approximate Backbone-Based Clustering Algorithm for Tags', Advanced Data Mining and Applications, Lecture Notes in Computer Science, International Conference on Advanced Data Mining and Applications, Springer Berlin Heidelberg, Beijing, China, pp. 175-189.
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In social annotation systems, users label digital resources by using tags which are freely chosen textual descriptions. Tags are used to index, anno- tate and retrieve resource as an additional metadata of resource . Poor retrieval performance remains a major problem of most social tagging systems resulting from the severe difficulty of ambiguity, redundancy and less semantic nature of tags. Clustering method is a useful tool to address the aforementioned difficul- ties. Most of the researches on tag cluste ring are directly using traditional clus- tering algorithms such as K-means or Hierarchical Agglomerative Clustering on tagging data, which possess the inherent drawbacks, such as the sensitivity of initialization. In this paper, we instead make use of the approximate backbone of tag clustering results to find out better tag clusters. In particular, we propose an APProximate backbonE-based Clustering algorithm for Tags (APPECT). The main steps of APPECT are: (1) we execute the K-means algorithm on a tag similarity matrix for M times and collect a set of tag clustering results Z={C 1 ,C 2 ,...,C m } ; (2) we form the approximate backbone of Z by executing a greedy search; (3) we fix the approximate backbone as the initial tag clustering result and then assign the rest tags into the corresponding clusters based on the similarity. Experimental results on three real world datasets namely MedWorm, MovieLens and Dmoz demonstrate the effectiveness and the superiority of the proposed method against the traditional approaches.
