Anandagopu, P, Banu, S & Li, J 2010, 'Low thymine content in PINK1 mRNAs and insights into Parkinson’s disease', Bioinformation, vol. 4, no. 10, pp. 452-455.
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Thymine is the only nucleotide base which is changed to uracil upon transcription, leaving mRNA less hydrophobic compared to its DNA counterpart. All the 16 codons that contain uracil (or thymine in gene) as the second nucleotide code for the five large hydrophobic residues (LHRs), namely phenylalanine,v isoleucine, leucine, methionine and valine. Thymine content (i.e. the fraction of XTX codons, where X = A, C, G, or T) in PINK1 mRNA sequences and its relationship with protein stability and function are the focus of this work. This analysis will shed light on PINK1's stability, thus a clue can be provided to understand the mitochondrial dysfunction and the failure of oxidative stress control frequently observed in Parkinson's disease. We obtained the complete PINK1 mRNA sequences of 8 different species. The distributions of XTX codons in different frames are calculated. We observed that the thymine content reached the highest level in the coding frame 1 of the PINK1 mRNA sequence of Bos Taurus (Bt), that is peaked at 27%. Coding frame 1 containing low thymine leads to the reduction in LHRs in the corresponding proteins. Therefore, we conjecture that proteins from the other organisms, including Homo sapiens, lost some of their hydrophobicity and became susceptible to dysfunction. Genes such as PINK1 have reduced thymine in the evolutionary process thereby making their protein products potentially being susceptible to instability and causing disease. Adding more hydrophobic residues (thymine) at appropriate places might help conserve important biological functions.
Budka, M, Gabrys, B & Ravagnan, E 2010, 'Robust predictive modelling of water pollution using biomarker data', Water Research, vol. 44, no. 10, pp. 3294-3308.
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Cai, T, Parast, L & Ryan, L 2010, 'Meta-analysis for rare events', STATISTICS IN MEDICINE, vol. 29, no. 20, pp. 2078-2089.
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Meta-analysis provides a useful framework for combining information across related studies and has been widely utilized to combine data from clinical studies in order to evaluate treatment efficacy. More recently, meta-analysis has also been used to assess drug safety. However, because adverse events are typically rare, standard methods may not work well in this setting. Most popular methods use fixed or random effects models to combine effect estimates obtained separately for each individual study. In the context of very rare outcomes, effect estimates from individual studies may be unstable or even undefined. We propose alternative approaches based on Poisson random effects models to make inference about the relative risk between two treatment groups. Simulation studies show that the proposed methods perform well when the underlying event rates are low. The methods are illustrated using data from a recent meta-analysis (N. Engl. J. Med. 2007; 356(24):2457-2471) of 48 comparative trials involving rosiglitazone, a type 2 diabetes drug, with respect to its possible cardiovascular toxicity. Copyright © 2010 John Wiley & Sons, Ltd.
Cao, L 2010, 'Domain-Driven Data Mining: Challenges and Prospects', IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 6, pp. 755-769.
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Traditional data mining research mainly focus]es on developing, demonstrating, and pushing the use of specific algorithms and models. The process of data mining stops at pattern identification. Consequently, a widely seen fact is that 1) many algorithms have been designed of which very few are repeatable and executable in the real world, 2) often many patterns are mined but a major proportion of them are either commonsense or of no particular interest to business, and 3) end users generally cannot easily understand and take them over for business use. In summary, we see that the findings are not actionable, and lack soft power in solving real-world complex problems. Thorough efforts are essential for promoting the actionability of knowledge discovery in real-world smart decision making. To this end, domain-driven data mining (D3M) has been proposed to tackle the above issues, and promote the paradigm shift from ÃÂdata-centered knowledge discoveryÃÂ to ÃÂdomain-driven, actionable knowledge delivery.ÃÂ In D3M, ubiquitous intelligence is incorporated into the mining process and models, and a corresponding problem-solving system is formed as the space for knowledge discovery and delivery. Based on our related work, this paper presents an overview of driving forces, theoretical frameworks, architectures, techniques, case studies, and open issues of D3M. We understand D3M discloses many critical issues with no thorough and mature solutions available for now, which indicates the challenges and prospects for this new topic.
Catchpoole, DR, Kennedy, P, Skillicorn, DB & Simoff, S 2010, 'The Curse of Dimensionality: A Blessing to Personalized Medicine', Journal of Clinical Oncology, vol. 28, no. 34, pp. e723-e724.
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Chen, P & Li, J 2010, 'Prediction of protein long-range contacts using an ensemble of genetic algorithm classifiers with sequence profile centers', BMC Structural Biology, vol. 10, no. Suppl 1, pp. S2-S2.
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Background. Prediction of long-range inter-residue contacts is an important topic in bioinformatics research. It is helpful for determining protein structures, understanding protein foldings, and therefore advancing the annotation of protein functions. Results. In this paper, we propose a novel ensemble of genetic algorithm classifiers (GaCs) to address the long-range contact prediction problem. Our method is based on the key idea called sequence profile centers (SPCs). Each SPC is the average sequence profiles of residue pairs belonging to the same contact class or non-contact class. GaCs train on multiple but different pairs of long-range contact data (positive data) and long-range non-contact data (negative data). The negative data sets, having roughly the same sizes as the positive ones, are constructed by random sampling over the original imbalanced negative data. As a result, about 21.5% long-range contacts are correctly predicted. We also found that the ensemble of GaCs indeed makes an accuracy improvement by around 5.6% over the single GaC. Conclusions. Classifiers with the use of sequence profile centers may advance the long-range contact prediction. In line with this approach, key structural features in proteins would be determined with high efficiency and accuracy. © 2010 Li and Chen; licensee BioMed Central Ltd.
Chen, P & Li, J 2010, 'Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information', BMC Bioinformatics, vol. 11, no. 1, pp. 0-0.
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Background: Protein-protein interactions play essential roles in protein function determination and drug design. Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost. Therefore, it is important to improve the performance for predicting protein interaction sites based on primary sequence alone.Results: We propose a new idea to construct an integrative profile for each residue in a protein by combining its hydrophobic and evolutionary information. A support vector machine (SVM) ensemble is then developed, where SVMs train on different pairs of positive (interface sites) and negative (non-interface sites) subsets. The subsets having roughly the same sizes are grouped in the order of accessible surface area change before and after complexation. A self-organizing map (SOM) technique is applied to group similar input vectors to make more accurate the identification of interface residues. An ensemble of ten-SVMs achieves an MCC improvement by around 8% and F1 improvement by around 9% over that of three-SVMs. As expected, SVM ensembles constantly perform better than individual SVMs. In addition, the model by the integrative profiles outperforms that based on the sequence profile or the hydropathy scale alone. As our method uses a small number of features to encode the input vectors, our model is simpler, faster and more accurate than the existing methods.Conclusions: The integrative profile by combining hydrophobic and evolutionary information contributes most to the protein-protein interaction prediction. Results show that evolutionary context of residue with respect to hydrophobicity makes better the identification of protein interface residues. In addition, the ensemble of SVM classifiers improves the prediction performance.Availability: Datasets and software are available at http://mail.ustc.edu.cn/~bigeagle/BMCBioinfo2010/index.ht...
Chen, P, Liu, C, Burge, L, Li, J, Mohammad, M, Southerland, W, Gloster, C & Wang, B 2010, 'DomSVR: domain boundary prediction with support vector regression from sequence information alone', Amino Acids, vol. 39, no. 3, pp. 713-726.
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Protein domains are structural and fundamental functional units of proteins. The information of protein domain boundaries is helpful in understanding the evolution, structures and functions of proteins, and also plays an important role in protein classification. In this paper, we propose a support vector regression-based method to address the problem of protein domain boundary identification based on novel input profiles extracted from AA-index database. As a result, our method achieves an average sensitivity of ∼36.5% and an average specificity of ∼ 81% for multi-domain protein chains, which is overall better than the performance of published approaches to identify domain boundary. As our method used sequence information alone, our method is simpler and faster.© Springer-Verlag 2010.
Chen, Y-C, Christiani, DC, Su, H-JJ, Hsueh, Y-M, Smith, TJ, Ryan, LM, Chao, S-C, Lee, JY-Y & Guo, Y-LL 2010, 'Early-life or lifetime sun exposure, sun reaction, and the risk of squamous cell carcinoma in an Asian population', CANCER CAUSES & CONTROL, vol. 21, no. 5, pp. 771-776.
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Background It has been widely accepted that sun exposure is a risk factor of squamous cell carcinoma (SCC) among fair-skinned populations. However, sun exposure and sun reaction have not been explored in Asians and no gender-specific data were available. Method In a case-control study, 176 incident skin cancer cases were recruited from National Cheng-Kung University Medical Center from 1996 to 1999. Controls included 216 age-, gender-, and residency-matched subjects from the southwestern Taiwan. A questionnaire was administered to collect information on life style and other risk factors. Logistic regression analysis was performed to evaluate the association between sun exposure or sun reaction and the risk of SCC by gender. Results Early-age (age 15 to 24) and lifetime sun exposure were significantly associated with increased risk of SCC in a dose-response pattern [odds ratio (OR) = 1.49-3.08, trend p = 0.009 and 0.0007, respectively]. After stratified by gender, the third tertile of early-age sun exposure was significantly associated with the SCC risk among men (OR = 3.08). The second and third tertiles of lifetime sun exposure was significantly associated with SCC risk among women (OR = 3.78 and 4.53, respectively). Skin reaction after 2-h sun exposure during childhood and adolescence was not significantly associated with the risk of SCC. Conclusions Lifetime sun exposure was more related to SCC risk in women, while early-age sun exposure was more relevant to men's SCC risk. This may be attributable to different lifestyle between men and women. © Springer Science+Business Media B.V. 2010.
Chenqi Zhang, Yu, PS & Bell, D 2010, 'Introduction to the Domain-Drive Data Mining Special Section', IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 6, pp. 753-754.
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IN the last decade, data mining has emerged as one of the most dynamic and lively areas in information technology. Although many algorithms and techniques for data mining have been proposed, they either focus on domainindependent techniques or on very specific domain problems. A general requirement in bridging the gap between academia and business is to cater to general domain-related issues surrounding real-life applications, such as constraints, organizational factors, domain expert knowledge, domain adaption, and operational knowledge. Unfortunately, these either have not been addressed, or have not been sufficiently addressed, in current data mining research and development. By common consent, experience seems to indicate that real-world data mining must, in the majority of cases, consider and involve the domain expertsâ role, domain knowledge, business intelligence, human intelligence, network intelligence, social intelligence, domain-specific constraints, as well as organizational factors and social issues in practice. However, it is difficult to merge the above domain factors with data mining models and processes. It is also challenging to discover knowledge that will support users to take decision-making actions.
Da Xu, RY & Kemp, M 2010, 'Fitting Multiple Connected Ellipses to an Image Silhouette Hierarchically', IEEE Transactions on Image Processing, vol. 19, no. 7, pp. 1673-1682.
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In this paper, we seek to fit a model, specified in terms of connected ellipses, to an image silhouette. Some algorithms that have attempted this problem are sensitive to initial guesses and also may converge to a wrong solution when they attempt to mini
Feng, M, Dong, G, Li, J, Tan, Y-P & Wong, L 2010, 'PATTERN SPACE MAINTENANCE FOR DATA UPDATES AND INTERACTIVE MINING', COMPUTATIONAL INTELLIGENCE, vol. 26, no. 3, pp. 282-317.
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This article addresses the incremental and decremental maintenance of the frequent pattern space. We conduct an in-depth investigation on how the frequent pattern space evolves under both incremental and decremental updates. Based on the evolution analysis, a new data structure, Generator-Enumeration Tree (GE-tree), is developed to facilitate the maintenance of the frequent pattern space. With the concept of GE-tree, we propose two novel algorithms, Pattern Space Maintainer+ (PSM+) and Pattern Space Maintainer- (PSM-), for the incremental and decremental maintenance of frequent patterns. Experimental results demonstrate that the proposed algorithms, on average, outperform the representative state-of-the-art methods by an order of magnitude. © 2010 Wiley Periodicals, Inc.
Geng, X, Smith-Miles, K, Wang, L, Li, M & Wu, Q 2010, 'Context-aware fusion: A case study on fusion of gait and face for human identification in video', Pattern Recognition, vol. 43, no. 10, pp. 3660-3673.
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Most work on multi-biometric fusion is based on static fusion rules. One prominent limitation of static fusion is that it cannot respond to the changes of the environment or the individual users. This paper proposes context-aware multi-biometric fusion, which can dynamically adapt the fusion rules to the real-time context. As a typical application, the context-aware fusion of gait and face for human identification in video is investigated. Two significant context factors that may affect the relationship between gait and face in the fusion are considered, i.e., view angle and subject-to-camera distance. Fusion methods adaptable to these two factors based on either prior knowledge or machine learning are proposed and tested. Experimental results show that the context-aware fusion methods perform significantly better than not only the individual biometric traits, but also those widely adopted static fusion rules including SUM, PRODUCT, MIN, and MAX. Moreover, context-aware fusion based on machine learning shows superiority over that based on prior knowledge. © 2010 Elsevier Ltd. All rights reserved.
Gil-Lafuente, AM & Merigó, JM 2010, 'Decision Making Techniques in Political Management', Studies in Fuzziness and Soft Computing, vol. 254, pp. 389-405.
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In this paper, we develop a new decision making model and apply it in political management. We use a framework based on the use of ideals in the decision process and several similarity measures such as the Hamming distance, the adequacy coefficient and the index of maximum and minimum level. For each similarity measure, we use different types of aggregation operators such as the simple average, the weighted average, the ordered weighted averaging (OWA) operator and the generalized OWA (GOWA) operator. This new approach considers several attributes and different scenarios that may occur in the uncertain environment. We see that depending on the particular type of aggregation operator used the results may lead to different decisions. © 2010 Springer-Verlag Berlin Heidelberg.
Juszczyszyn, K, Kazienko, P & Musiał, K 2010, 'Personalized Ontology-Based Recommender Systems for Multimedia Objects', Studies in Computational Intelligence, vol. 289, pp. 275-292.
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A framework for recommendation of multimedia objects based on processing of individual ontologies is proposed in the chapter. The recommendation process takes into account similarities calculated both between objects' and users' ontologies, which reflect the social and semantic features existing in the system. The ontologies, which are close to the current context, provide a list of suggestions presented to the user. Each user in the system possesses its own Personal Agent that performs all necessary online tasks. Personal Agents co-operate each other and enrich lists of possible recommendations. The system was developed for the use inthe Flickr multimedia sharing system. © 2010 Springer-Verlag Berlin Heidelberg.
Lemke, C & Gabrys, B 2010, 'Meta-learning for time series forecasting and forecast combination', Neurocomputing, vol. 73, no. 10-12, pp. 2006-2016.
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Li, Z & Li, J 2010, 'Geometrically centered region: A “wet” model of protein binding hot spots not excluding water molecules', Proteins: Structure, Function, and Bioinformatics, vol. 78, no. 16, pp. 3304-3316.
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AbstractA protein interface can be as “wet” as a protein surface in terms of the number of immobilized water molecules. This important water information has not been explicitly taken by computational methods to model and identify protein binding hot spots, overlooking the water role in forming interface hydrogen bonds and in filing cavities. Hot spot residues are usually clustered at the core of the protein binding interfaces. However, traditional machine learning methods often identify the hot spot residues individually, breaking the cooperativity of the energetic contribution. Our idea in this work is to explore the role of immobilized water and meanwhile to capture two essential properties of hot spots: the compactness in contact and the far distance from bulk solvent. Our model is named geometrically centered region (GCR). The detection of GCRs is based on novel tripartite graphs, and atom burial levels which are a concept more intuitive than SASA. Applying to a data set containing 355 mutations, we achieved an F measure of 0.6414 when ΔΔG ≥ 1.0 kcal/mol was used to define hot spots. This performance is better than Robetta, a benchmark method in the field. We found that all but only one of the GCRs contain water to a certain degree, and most of the outstanding hot spot residues have water‐mediated contacts. If the water is excluded, the burial level values are poorly related to the ΔΔG, and the model loses its performance remarkably. We also presented a definition for the O‐ring of a GCR as the set of immediate neighbors of the residues in the GCR. Comparative analysis between the O‐rings and GCRs reveals that the newly defined O‐ring is indeed energetically less important than the GCR hot spot, confirming a long‐standing hypothesis. Proteins 2010. © 2010 Wiley‐Liss, Inc.
Liu, Q & Li, J 2010, 'Propensity vectors of low‐ASA residue pairs in the distinction of protein interactions', Proteins: Structure, Function, and Bioinformatics, vol. 78, no. 3, pp. 589-602.
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AbstractWe introduce low‐ASA residue pairs as classification features for distinguishing the different types of protein interactions. A low‐ASA residue pair is defined as two contact residues each from one chain that have a small solvent accessible surface area (ASA). This notion of residue pairs is novel as it first combines residue pairs with the O‐ring theory, an influential proposition stating that the binding hot spots at the interface are often surrounded by a ring of energetically less important residues. As binding hot spots lie in the core of the stability for protein interactions, we believe that low‐ASA residue pairs can sharpen the distinction of protein interactions. The main part of our feature vector is 210‐dimensional, consisting of all possible low‐ASA residue pairs; the value of every feature is determined by a propensity measure. Our classification method is called OringPV, which uses propensity vectors of protein interactions for support vector machine. OringPV is tested on three benchmark datasets for a variety of classification tasks such as the distinction between crystal packing and biological interactions, the distinction between two different types of biological interactions, etc. The evaluation frameworks include within‐dataset, cross‐dataset comparison, and leave‐one‐out cross‐validation. The results show that low‐ASA residue pairs and the propensity vector description of protein interactions are truly strong in the distinction. In particular, many cross‐dataset generalization capability tests have achieved excellent recalls and overall accuracies, much outperforming existing benchmark methods. Proteins 2010. © 2009 Wiley‐Liss, Inc.
Liu, Q & Li, J 2010, 'Protein binding hot spots and the residue-residue pairing preference: a water exclusion perspective', BMC Bioinformatics, vol. 11, no. 1.
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Longbing Cao, Yanchang Zhao, Huaifeng Zhang, Dan Luo, Chengqi Zhang & Park, EK 2010, 'Flexible Frameworks for Actionable Knowledge Discovery', IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 9, pp. 1299-1312.
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Most data mining algorithms and tools stop at the mining and delivery of patterns satisfying expected technical interestingness. There are often many patterns mined but business people either are not interested in them or do not know what follow-up actio
Luo, F, Liu, J & Li, J 2010, 'Discovering conditional co-regulated protein complexes by integrating diverse data sources.', BMC Syst Biol, vol. 4 Suppl 2, no. Suppl 2, pp. S4-13.
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BACKGROUND: Proteins interacting with each other as a complex play an important role in many molecular processes and functions. Directly detecting protein complexes is still costly, whereas many protein-protein interaction (PPI) maps for model organisms are available owing to the fast development of high-throughput PPI detecting techniques. These binary PPI data provides fundamental and abundant information for inferring new protein complexes. However, PPI data from different experiments do not overlap very much usually. The main reason is that the functions of proteins can activate only on certain environment or stimulus. In a short, PPI is condition-specific. Therefore specifying the conditions on when complexes are present is necessary for a deep understanding of their behaviours. Meanwhile, proteins have various interaction ways and control mechanisms to form different kinds of complexes. Thus the discovery of a certain type of complexes should depend on their own distinct biological or topological characteristics. We do not attempt to find all kinds of complexes by using certain features. Here, we integrate transcription regulation data (TR), gene expression data (GE) and protein-protein interaction data at the systems biology level to discover a special kind of protein complex called conditional co-regulated protein complexes. A conditional co-regulated protein complex has three remarkable features: the coding genes of the member proteins share the same transcription factor (TF), under a certain condition the coding genes express co-ordinately and the member proteins interact mutually as a complex to implement a common biological function. RESULTS: A framework of discovering the conditional co-regulated protein complexes is proposed. Testing on the Yeast data sets under the Cell Cycle, DNA Damage and Dauxic Shift conditions, we identified a total of 29 conditional co-regulated complexes, among which the coding genes in 14 complexes show a strong a...
Mann, S, Li, J & Chen, Y-PP 2010, 'Insights into Bacterial Genome Composition through Variable Target GC Content Profiling', Journal of Computational Biology, vol. 17, no. 1, pp. 79-96.
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This study presents a new computational method for guanine (G) and cytosine (C), or GC, content profiling based on the idea of multiple resolution sampling (MRS). The benefit of our new approach over existing techniques follows from its ability to locate
Merigó Lindahl, JM & Casanovas Ramón, M 2010, 'The generalized hybrid averaging operator and its application in decision making', Revista de Metodos Cuantitativos para la Economia y la Empresa, vol. 9, no. 1, pp. 69-84.
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We present the generalized hybrid averaging (GHA) operator. It is a new aggregation operator that generalizes the hybrid averaging (HA) operator by using the generalized mean. Thus, we are able to generalize a wide range of mean operators such as the HA, the hybrid geometric averaging (HGA), the hybrid quadratic averaging (HQA), the generalized ordered weighted averaging (GOWA) operator and the weighted generalized mean (WGM). A key feature in this aggregation operator is that it is able to deal with the weighted average and the ordered weighted averaging (OWA) operator in the same formulation. We further generalize the GHA by using quasi-arithmetic means obtaining the quasi-arithmetic hybrid averaging (Quasi-HA) operator. We conclude the paper with an example of the new approach in a financial decision making problem.
Merigó, JM 2010, 'Fuzzy decision making with immediate probabilities', Computers & Industrial Engineering, vol. 58, no. 4, pp. 651-657.
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Merigo, JM & Casanovas, M 2010, 'Induced and heavy aggregation operators with distance measures', Journal of Systems Engineering and Electronics, vol. 21, no. 3, pp. 431-439.
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Merigó, JM & Casanovas, M 2010, 'Decision making with distance measures and linguistic aggregation 0operators', International Journal of Fuzzy Systems, vol. 12, no. 3, pp. 190-198.
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We present a new decision making model with distance measures by using linguistic aggregation operators. We introduce a new aggregation operator called the linguistic ordered weighted averaging distance (LOWAD) operator. This aggregation operator provides a parameterized family of blinguistic aggregation operators that includes the maximum distance, the minimum distance, the linguistic normalized Hamming distance and the linguistic weighted Hamming distance, among others. We study some of its main properties and different families of LOWAD operators such as the median-LOWAD, the Olympic-LOWAD, the S-LOWAD and the centered-LOWAD. We also develop an application of the new approach in a decision making problem concerning human resource management. © 2010 TFSA.
Merigó, JM & Casanovas, M 2010, 'Fuzzy generalized hybrid aggregation operators and its application in fuzzy decision making', International Journal of Fuzzy Systems, vol. 12, no. 1, pp. 15-24.
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The hybrid averaging (HA) is an aggregation operator that uses the weighted average (WA) and the ordered weighted averaging (OWA) operator in the same formulation. In this paper, we introduce several generalizations of the HA operator by using generalized and quasi-arithmetic means, fuzzy numbers and order inducing variables in the reordering step of the aggregation process. We present the fuzzy generalized hybrid averaging (FGHA) operator, the fuzzy induced generalized hybrid averaging (FIGHA) operator, the Quasi-FHA operator and the Quasi-FIHA operator. The main advantage of these operators is that they generalize a wide range of fuzzy aggregation operators that can be used in a wide range of applications such as decision making problems. For example, we could mention the fuzzy induced hybrid averaging (FIHA), the fuzzy weighted generalized mean (FWGM) and the fuzzy induced generalized OWA (FIGOWA). We end the paper with an application of the new approach in a decision making problem. © 2010 TFSA.
Merigó, JM & Casanovas, M 2010, 'THE FUZZY GENERALIZED OWA OPERATOR AND ITS APPLICATION IN STRATEGIC DECISION MAKING', Cybernetics and Systems, vol. 41, no. 5, pp. 359-370.
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Merigó, JM & Gil-Lafuente, AM 2010, 'New decision-making techniques and their application in the selection of financial products', Information Sciences, vol. 180, no. 11, pp. 2085-2094.
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MERIGÓ, JM, CASANOVAS, M & MARTÍNEZ, L 2010, 'LINGUISTIC AGGREGATION OPERATORS FOR LINGUISTIC DECISION MAKING BASED ON THE DEMPSTER-SHAFER THEORY OF EVIDENCE', International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 18, no. 03, pp. 287-304.
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In this paper, we develop a new approach for decision making with Dempster-Shafer theory of evidence by using linguistic information. We suggest the use of different types of linguistic aggregation operators in the model. We then obtain as a result, the belief structure — linguistic ordered weighted averaging (BS-LOWA), the BS — linguistic hybrid averaging (BS-LHA) and a wide range of particular cases. Some of their main properties are studied. Finally, we provide an illustrative example that shows the different results obtained by using different types of linguistic aggregation operators in the new approach.
Merigó, JM, Gil Lafuente, AM & Barcellos, L 2010, 'UNCERTAIN INDUCED GENERALIZED AGGREGATION OPERATORS AND ITS APPLICATION IN THE THEORY OF EXPERTONS', FUZZY ECONOMIC REVIEW, vol. 15, no. 02, pp. 25-42.
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We present a new approach that unifies the induced generalized ordered weighted averaging (IGOWA) operator with the weighted average (WA) when the available information is uncertain and can be assessed with interval numbers. We call it the uncertain induced generalized ordered weighted averaging - weighted averaging (UIGOWAWA) operator. The main advantage of this approach is that it unifies the IOWA and the WA taking into account the degree of importance of each case in the formulation and considering that the information is given with interval numbers. We also study different properties of the UIGOWAWA operator and different particular cases. We also analyze the applicability of the new approach and we see that it is possible to develop a wide range of applications because all the previous studies that use the WA can be revised and extended with this new approach. We focus on an application in decision making with the theory of expertons. Thus, we are able to assess group decision making problems in a more complete way.
Milton, J & Kennedy, PJ 2010, 'Static and Dynamic Selection Thresholds Governing the Accumulation of Information in Genetic Algorithms Using Ranked Populations', EVOLUTIONARY COMPUTATION, vol. 18, no. 2, pp. 229-254.
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Mutation applied indiscriminately across a population has, on average, a detrimental effect on the accumulation of solution alleles within the population and is usually beneficial only when targeted at individuals with few solution alleles. Many common selection techniques can delete individuals with more solution alleles than are easily recovered by mutation. The paper identifies static and dynamic selection thresholds governing accumulation of information in a genetic algorithm (GA). When individuals are ranked by fitness, there exists a dynamic threshold defined by the solution density of surviving individuals and a lower static threshold defined by the solution density of the information source used for mutation. Replacing individuals ranked below the static threshold with randomly generated individuals avoids the need for mutation while maintaining diversity in the population with a consequent improvement in population fitness. By replacing individuals ranked between the thresholds with randomly selected individuals from above the dynamic threshold, population fitness improves dramatically. We model the dynamic behavior of GAs using these thresholds and demonstrate their effectiveness by simulation and benchmark problems.
Paisitkriangkrai, S, Shen, C & Zhang, J 2010, 'Incremental Training of a Detector Using Online Sparse Eigen-decomposition', IEEE Transactions on Image Processing, vol. 20, no. 1, pp. 213-226.
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The ability to efficiently and accurately detect objects plays a very crucialrole for many computer vision tasks. Recently, offline object detectors haveshown a tremendous success. However, one major drawback of offline techniquesis that a complete set of training data has to be collected beforehand. Inaddition, once learned, an offline detector can not make use of newly arrivingdata. To alleviate these drawbacks, online learning has been adopted with thefollowing objectives: (1) the technique should be computationally and storageefficient; (2) the updated classifier must maintain its high classificationaccuracy. In this paper, we propose an effective and efficient framework forlearning an adaptive online greedy sparse linear discriminant analysis (GSLDA)model. Unlike many existing online boosting detectors, which usually applyexponential or logistic loss, our online algorithm makes use of LDA's learningcriterion that not only aims to maximize the class-separation criterion butalso incorporates the asymmetrical property of training data distributions. Weprovide a better alternative for online boosting algorithms in the context oftraining a visual object detector. We demonstrate the robustness and efficiencyof our methods on handwriting digit and face data sets. Our results confirmthat object detection tasks benefit significantly when trained in an onlinemanner.
Pampanin, DM, Ravagnan, E, Apeland, S, Aarab, N, Godal, BF, Westerlund, S, Hjermann, DØ, Eftestøl, T, Budka, M, Gabrys, B, Viarengo, A & Barsiene, J 2010, 'The marine environment I.Q. concept', Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, vol. 157, pp. S52-S52.
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Sparks, R, Carter, C, Graham, P, Muscatello, D, Churches, T, Kaldor, J, Turner, R, Zheng, W & Ryan, L 2010, 'Understanding sources of variation in syndromic surveillance for early warning of natural or intentional disease outbreaks', IIE TRANSACTIONS, vol. 42, no. 9, pp. 613-631.
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Daily counts of computer records of hospital emergency department arrivals grouped according to diagnosis (called here syndrome groupings) can be monitored by epidemiologists for changes in frequency that could provide early warning of bioterrorism events or naturally occurring disease outbreaks and epidemics. This type of public health surveillance is sometimes called syndromic surveillance. We used transitional Poisson regression models to obtain one-day-ahead arrival forecasts. Regression parameter estimates and forecasts were updated for each day using the latest 365 days of data. The resulting time series of recursive estimates of parameters such as the amplitude and location of the seasonal peaks as well as the one-day-ahead forecasts and forecast errors can be monitored to understand changes in epidemiology of each syndrome grouping. The counts for each syndrome grouping were autocorrelated and non-homogeneous Poisson. As such, the main methodological contribution of the article is the adaptation of Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) plans for monitoring non-homogeneous counts. These plans were valid for small counts where the assumption of normally distributed one-day-ahead forecasts errors, typically used in other papers, breaks down. In addition, these adaptive plans have the advantage that control limits do not have to be trained for different syndrome groupings or aggregations of emergency departments. Conventional methods for signaling increases in syndrome grouping counts, Shewhart, CUSUM, and EWMA control charts of the standardized forecast errors were also examined. Shewhart charts were, at times, insensitive to shifts of interest. CUSUM and EWMA charts were only reasonable for large counts. We illustrate our methods with respiratory, influenza, diarrhea, and abdominal pain syndrome groupings. Copyright © 'IIE'.
Strauss, WJ, Ryan, L, Morara, M, Iroz-Elardo, N, Davis, M, Cupp, M, Nishioka, MG, Quackenboss, J, Galke, W, Ozkaynak, H & Scheidt, P 2010, 'Improving cost-effectiveness of epidemiological studies via designed missingness strategies', STATISTICS IN MEDICINE, vol. 29, no. 13, pp. 1377-1387.
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Modern epidemiological studies face opportunities and challenges posed by an ever-expanding capacity to measure a wide range of environmental exposures, along with sophisticated biomarkers of exposure and response at the individual level. The challenge of deciding what to measure is further complicated for longitudinal studies, where logistical and cost constraints preclude the collection of all possible measurements on all participants at every follow-up time. This is true for the National Children's Study (NCS), a large-scale longitudinal study that will enroll women both prior to conception and during pregnancy and collect information on their environment, their pregnancies, and their children's development through early adulthood - with a goal of assessing key exposure/outcome relationships among a cohort of approximately 100 000 children. The success of the NCS will significantly depend on the accurate, yet cost-effective, characterization of environmental exposures thought to be related to the health outcomes of interest. The purpose of this paper is to explore the use of cost saving, yet valid and adequately powered statistical approaches for gathering exposure information within epidemiological cohort studies. The proposed approach involves the collection of detailed exposure assessment information on a specially selected subset of the study population, and collection of less-costly, and presumably less-detailed and less-burdensome, surrogate measures across the entire cohort. We show that large-scale efficiency in costs and burden may be achieved without making substantive sacrifices on the ability to draw reliable inferences concerning the relationship between exposure and health outcome. Several detailed scenarios are provided that document how the targeted sub-sampling design strategy can benefit large cohort studies like the NCS, as well as other more focused environmental epidemiologic studies.
Xiaowen Liu, Jinyan Li & Lusheng Wang 2010, 'Modeling Protein Interacting Groups by Quasi-Bicliques: Complexity, Algorithm, and Application', IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 2, pp. 354-364.
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Protein-protein interactions (PPIs) are one of the most important mechanisms in cellular processes. To model protein interaction sites, recent studies have suggested to find interacting protein group pairs from large PPI networks at the first step and then to search conserved motifs within the protein groups to form interacting motif pairs. To consider the noise effect and the incompleteness of biological data, we propose to use quasi-bicliques for finding interacting protein group pairs. We investigate two new problems that arise from finding interacting protein group pairs: the maximum vertex quasi-biclique problem and the maximum balanced quasi-biclique problem. We prove that both problems are NP-hard. This is a surprising result as the widely known maximum vertex biclique problem is polynomial time solvable [1]. We then propose a heuristic algorithm that uses the greedy method to find the quasi-bicliques from PPI networks. Our experiment results on real data show that this algorithm has a better performance than a benchmark algorithm for identifying highly matched BLOCKS and PRINTS motifs. We also report results of two case studies on interacting motif pairs that map well with two interacting domain pairs in iPfam. Availability: The software and supplementary information are available at http://www.cs.cityu.edu.hk/~lwang/software/ppi/index.html. © 2006 IEEE.
Xu, RYD & Kemp, M 2010, 'An iterative approach for fitting multiple connected ellipse structure to silhouette', Pattern Recognition Letters, vol. 31, no. 13, pp. 1860-1867.
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In many image processing applications, the structures conveyed in the image contour can often be described by a set of connected ellipses. Previous fitting methods to align the connected ellipse structure with a contour, in general, lack a continuous solution space. In addition, the solution obtain often satisfies only a partial number of ellipses, leaving others with poor fits. In this paper, we address these two problems by presenting an iterative framework for fitting a 2D silhouettte contour to a pre-specified connected ellipses structure with a very coarse initial guess. Under the proposed framework, we first improve the initial guess by modelling the silhouette region as set of disconnected ellipses using mixture of Gaussian densities or the heuristics approaches. Then, an iterative method is applied in a similar fashion to the Iterative Closest Point (ICP) (Alshawa, 2007; Li and Griffiths, 2000; Besl and McKay, 1992) algorithm. Each iteration contains two parts: first part is to assighn all the contour points to the individual unconnected ellipses, which we refer to as the segmentation step and the second part is the non-linear least square approach that minimizes both the sum of the square distance between the countour points and ellipse's edge as well as minimizing the ellipse's vertex pair(s) distances, which we refer to as the minimization step. We illustrate the effectiveness of our menthods through experimental result on several images as well as applying the algorithm to a mini database of human upper-body images.
Yanq, T, Kecrnan, V & Cao, L 2010, 'Classification by ALH-fast algorithm', Tsinghua Science and Technology, vol. 15, no. 3, pp. 275-280.
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The adaptive local hyperplane (ALH) algorithm is a very recently proposed classifier, which has been shown to perform better than many other benchmarking classifiers including support vector machine (SVM), K-nearest neighbor (KNN), linear discriminant analysis (LDA), and K-local hyperplane distance nearest neighbor (HKNN) algorithms. Although the ALH algorithm is well formulated and despite the fact that it performs well in practice, its scalability over a very large data set is limited due to the online distance computations associated with all training instances. In this paper, a novel algorithm, called ALH-Fast and obtained by combining the classification tree algorithm and the ALH, is proposed to reduce the computational load of the ALH algorithm. The experiment results on two large data sets show that the ALH-Fast algorithm is both much faster and more accurate than the ALH algorithm.
Zhao, L & Li, J 2010, 'Mining for the antibody-antigen interacting associations that predict the B cell epitopes', BMC Structural Biology, vol. 10, no. Suppl 1, pp. S6-S6.
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Background. Predicting B-cell epitopes is very important for designing vaccines and drugs to fight against the infectious agents. However, due to the high complexity of this problem, previous prediction methods that focus on linear and conformational epitope prediction are both unsatisfactory. In addition, antigen interacting with antibody is context dependent and the coarse binary classification of antigen residues into epitope and non-epitope without the corresponding antibody may not reveal the biological reality. Therefore, we take a novel way to identify epitopes by using associations between antibodies and antigens. Results. Given a pair of antibody-antigen sequences, the epitope residues can be identified by two types of associations: paratope-epitope interacting biclique and cooccurrent pattern of interacting residue pairs. As the association itself does not include the neighborhood information on the primary sequence, residues' cooperativity and relative composition are then used to enhance our method. Evaluation carried out on a benchmark data set shows that the proposed method produces very good performance in terms of accuracy. After compared with other two structure-based B-cell epitope prediction methods, results show that the proposed method is competitive to, sometimes even better than, the structure-based methods which have much smaller applicability scope. Conclusions. The proposed method leads to a new way of identifying B-cell epitopes. Besides, this antibody-specified epitope prediction can provide more precise and helpful information for wet-lab experiments. © 2010 Li and Zhao; licensee BioMed Central Ltd.
Zong, Y, Li, M-C, Xu, G-D & Zhang, Y-C 2010, 'High Dimensional Clustering Algorithm Based on Local Significant Units', Journal of Electronics & Information Technology, vol. 32, no. 11, pp. 2707-2712.
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High dimensional clustering algorithm based on equal or random width density grid cannot guarantee high quality clustering results in complicated data sets. In this paper, a High dimensional Clustering algorithm based on Local Significant Unit (HC_LSU) is proposed to deal with this problem, based on the kernel estimation and spatial statistical theory. Firstly, a structure, namely Local Significant Unit (LSU) is introduced by local kernel density estimation and spatial statistical test; secondly, a greedy algorithm named Greedy Algorithm for LSU (GA_LSU) is proposed to quickly find out the local significant units in the data set; and eventually, the single-linkage algorithm is run on the local significant units with the same attribute subset to generate the clustering results. Experimental results on 4 synthetic and 6 real world data sets showed that the proposed high-dimensional clustering algorithm, HC_LSU, could effectively find out high quality clustering results from the highly complicated data sets.
Zong, Y, Xu, G, Zhang, Y, Jiang, H & Li, M 2010, 'A robust iterative refinement clustering algorithm with smoothing search space', Knowledge-Based Systems, vol. 23, no. 5, pp. 389-396.
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Iterative refinement clustering algorithms are widely used in data mining area, but they are sensitive to the initialization. In the past decades, many modified initialization methods have been proposed to reduce the influence of initialization sensitivity problem. The essence of iterative refinement clustering algorithms is the local search method. The big numbers of the local minimum points which are embedded in the search space make the local search problem hard and sensitive to the initialization. The smaller number of local minimum points, the more robust of initialization for a local search algorithm is. In this paper, we propose a TopDown Clustering algorithm with Smoothing Search Space (TDCS3) to reduce the influence of initialization. The main steps of TDCS3 are to: (1) dynamically reconstruct a series of smoothed search spaces into a hierarchical structure by `filling the local minimum points; (2) at the top level of the hierarchical structure, an existing iterative refinement clustering algorithm is run with random initialization to generate the clustering result; (3) eventually from the second level to the bottom level of the hierarchical structure, the same clustering algorithm is run with the initialization derived from the previous clustering result. Experiment results on 3 synthetic and 10 real world data sets have shown that TDCS3 has significant effects on finding better, robust clustering result and reducing the impact of initialization.
AlAamri, H, Abolhasan, M, Wysocki, T & Lipman, J 1970, 'On Optimising Route Discovery for Multi-interface and Power-Aware Nodes in Heterogeneous MANETs', 2010 6th International Conference on Wireless and Mobile Communications, 2010 6th International Conference on Wireless and Mobile Communications (ICWMC), IEEE, Valencia, pp. 244-249.
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This paper presents a new routing discovery strategy for heterogeneous MANETs. Node heterogeneity is modeled in terms of: types and number of different interfaces, power, and transmission ranges. Our proposed route discovery algorithm is implemented on the top of On-demand Tree-based Routing Protocol (OTRP) and hence it is called OTRP Heterogeneity-Aware (OTRP-HA). OTRP-HA utilizes node heterogeneity and optimizes route discovery to reduce overheads and ensures connectivities between different types of nodes with different interfaces. Each node makes its own decision to participate in the route discovery process according to its location, local density, and available resources. Simulation results show that OTRP-HA outperforms OTRP and AODV and it reduces overheads as a number of nodes and traffic increase, while it also further prolongs the lifetime of battery-powered single-interface nodes when compared to AODV. © 2010 IEEE.
Anaissi, A, Kennedy, PJ & Goyal, M 1970, 'A framework for high dimensional data reduction in the microarray domain', 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), IEEE, Changsha, China, pp. 903-907.
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Microarray analysis and visualization is very helpful for biologists and clinicians to understand gene expression in cells and to facilitate diagnosis and treatment of patients. However, a typical microarray dataset has thousands of features and a very small number of observations. This very high dimensional data has a massive amount of information which often contains some noise, non-useful information and small number of relevant features for disease or genotype. This paper proposes a framework for very high dimensional data reduction based on three technologies: feature selection, linear dimensionality reduction and non-linear dimensionality reduction. In this paper, feature selection based on mutual information will be proposed for filtering features and selecting the most relevant features with the minimum redundancy. A kernel linear dimensionality reduction method is also used to extract the latent variables from a high dimensional data set. In addition, a non-linear dimensionality reduction based on local linear embedding is used to reduce the dimension and visualize the data. Experimental results are presented to show the outputs of each step and the efficiency of this framework.
Bródka, P, Musial, K & Kazienko, P 1970, 'A Method for Group Extraction in Complex Social Networks', Communications in Computer and Information Science, Springer Berlin Heidelberg, pp. 238-247.
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The extraction of social groups from social networks existing among employees in the company, its customers or users of various computer systems became one of the research areas of growing importance. Once we have discovered the groups, we can utilise them, in different kinds of recommender systems or in the analysis of the team structure and communication within a given population. The shortcomings of the existing methods for community discovery and lack of their applicability in multi-layered social networks were the inspiration to create a new group extraction method in complex multi-layered social networks. The main idea that stands behind this new concept is to utilise the modified version of a measure called by authors multi-layered clustering coefficient. © 2010 Springer-Verlag.
Budka, M & Gabrys, B 1970, 'Correntropy-based density-preserving data sampling as an alternative to standard cross-validation', The 2010 International Joint Conference on Neural Networks (IJCNN), 2010 International Joint Conference on Neural Networks (IJCNN), IEEE.
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Estimation of the generalization ability of a predictive model is an important issue, as it indicates expected performance on previously unseen data and is also used for model selection. Currently used generalization error estimation procedures like cross-validation (CV) or bootstrap are stochastic and thus require multiple repetitions in order to produce reliable results, which can be computationally expensive if not prohibitive. The correntropy-based Density Preserving Sampling procedure (DPS) proposed in this paper eliminates the need for repeating the error estimation procedure by dividing the available data into subsets, which are guaranteed to be representative of the input dataset. This allows to produce low variance error estimates with accuracy comparable to 10 times repeated cross-validation at a fraction of computations required by CV, which has been investigated using a set of publicly available benchmark datasets and standard classifiers. © 2010 IEEE.
Budka, M & Gabrys, B 1970, 'Ridge regression ensemble for toxicity prediction', Procedia Computer Science, International Conference on Computational Science (ICCS), Elsevier BV, Univ Amsterdam, Amsterdam, NETHERLANDS, pp. 193-201.
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Cao, L, Ou, Y, Yu, PS & Wei, G 1970, 'Detecting abnormal coupled sequences and sequence changes in group-based manipulative trading behaviors', Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10: The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Washington DC, DC, USA, pp. 85-93.
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In capital market surveillance, an emerging trend is that a group of hidden manipulators collaborate with each other to manipulate three trading sequences: buy-orders, sell-orders and trades, through carefully arranging their prices, volumes and time, in order to mislead other investors, affect the instrument movement, and thus maximize personal benefits. If the focus is on only one of the above three sequences in attempting to analyze such hidden group based behavior, or if they are merged into one sequence as per an investor, the coupling relationships among them indicated through trading actions and their prices/volumes/times would be missing, and the resulting findings would have a high probability of mismatching the genuine fact in business. Therefore, typical sequence analysis approaches, which mainly identify patterns on a single sequence, cannot be used here. This paper addresses a novel topic, namely coupled behavior analysis in hidden groups. In particular, we propose a coupled Hidden Markov Models (HMM)-based approach to detect abnormal group-based trading behaviors. The resulting models cater for (1) multiple sequences from a group of people, (2) interactions among them, (3) sequence item properties, and (4) significant change among coupled sequences. We demonstrate our approach in detecting abnormal manipulative trading behaviors on orderbook-level stock data. The results are evaluated against alerts generated by the exchange's surveillance system from both technical and computational perspectives. It shows that the proposed coupled and adaptive HMMs outperform a standard HMM only modeling any single sequence, or the HMM combining multiple single sequences, without considering the coupling relationship. Further work on coupled behavior analysis, including coupled sequence/event analysis, hidden group analysis and behavior dynamics are very critical. © 2010 ACM.
Chen, X, Yang, J, Wu, Q & Zhao, J 1970, 'Motion blur detection based on lowest directional high-frequency energy', 2010 IEEE International Conference on Image Processing, 2010 17th IEEE International Conference on Image Processing (ICIP 2010), IEEE, Hongkong, pp. 2533-2536.
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Motion blur detection and the relevant blurring parameter estimation are important for many computer vision tasks. The contribution of this paper is in two folds. First, we propose a closed-form solution for motion direction estimation on blurred image. Secondly, a novel method is proposed for motion blurred region detection. The proposed direction estimation is based on measurement of lowest directional high-frequency energy. Compared with traditional methods, it will improve accuracy with less computational cost. Moreover, the proposed motion blurred region detection can efficiently estimate blurred regions without Point Spread Function estimation. Encouraging results are shown by experiments. © 2010 IEEE.
Concha, OP, Xu, RYD & Piccardi, M 1970, 'Compressive Sensing of Time Series for Human Action Recognition', 2010 International Conference on Digital Image Computing: Techniques and Applications, 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE, Sydney, Australia, pp. 454-461.
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Compressive Sensing (CS) is an emerging signal processing technique where a sparse signal is reconstructed from a small set of random projections. In the recent literature, CS techniques have demonstrated promising results for signal compression and reconstruction [9, 8, 1]. However, their potential as dimensionality reduction techniques for time series has not been significantly explored to date. To this aim, this work investigates the suitability of compressive-sensed time series in an application of human action recognition. In the paper, results from several experiments are presented: (1) in a first set of experiments, the time series are transformed into the CS domain and fed into a hidden Markov model (HMM) for action recognition; (2) in a second set of experiments, the time series are explicitly reconstructed after CS compression and then used for recognition; (3) in the third set of experiments, the time series are compressed by a hybrid CS-Haar basis prior to input into HMM; (4) in the fourth set, the time series are reconstructed from the hybrid CS-Haar basis and used for recognition. We further compare these approaches with alternative techniques such as sub-sampling and filtering. Results from our experiments show unequivocally that the application of CS does not degrade the recognition accuracy; rather, it often increases it. This proves that CS can provide a desirable form of dimensionality reduction in pattern recognition over time series. © 2010 Crown Copyright.
Concha, OP, Xu, RYD & Piccardi, M 1970, 'Robust Dimensionality Reduction for Human Action Recognition', 2010 International Conference on Digital Image Computing: Techniques and Applications, 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE, Sydney, Australia, pp. 349-356.
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Human action recognition can be approached by combining an action-discriminative feature set with a classifier. However, the dimensionality of typical feature sets joint with that of the time dimension often leads to a curse-of-dimensionality situation. Moreover, the measurement of the feature set is subject to sometime severe errors. This paper presents an approach to human action recognition based on robust dimensionality reduction. The observation probabilities of hidden Markov models (HMM) are modelled by mixtures of probabilistic principal components analyzers and mixtures of t-distribution sub-spaces, and compared with conventional Gaussian mixture models. Experimental results on two datasets show that dimensionality reduction helps improve the classification accuracy and that the heavier-tailed t-distribution can help reduce the impact of outliers generated by segmentation errors. © 2010 Crown Copyright.
Dong, H, Hussain, FK & Chang, E 1970, 'Semantic Service Retrieval and QoS Measurement in the Digital Ecosystem Environment', 2010 International Conference on Complex, Intelligent and Software Intensive Systems, 2010 International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), IEEE, pp. 153-160.
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Digital Ecosystem is an innovative high-tech environment with the purpose of supporting the activities among species within the business ecosystem. In this paper, we concern about the research issue of service retrieval within such an environment. Due to the fact that species are heterogeneous and geographically dispersed, to precisely and quickly locate a service provider becomes an issue. In addition, the Digital Ecosystem environment urgently requires the structualization of service information and a set of unified QoS measurement for service ranking and evaluation. In order to unfold the issues in detail, we use the means of case study and literature survey. Eventually we formulate the research issues in this domain and provide a possible solution. © 2010 IEEE.
Du, R, Wang, S, Wu, Q & He, X 1970, 'Learn Concepts in Multiple-Instance Learning with Diverse Density Framework Using Supervised Mean Shift', 2010 International Conference on Digital Image Computing: Techniques and Applications, 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE, Sydney, Australia, pp. 643-648.
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Many machine learning tasks can be achieved by using Multiple-instance learning (MIL) when the target features are ambiguous. As a general MIL framework, Diverse Density (DD) provides a way to learn those ambiguous features by maxmising the DD estimator, and the maximum of DD estimator is called a concept. However, modeling and finding multiple concepts is often difficult especially without prior knowledge of concept number, i.e., every positive bag may contain multiple coexistent and heterogeneous concepts but we do not know how many concepts exist. In this work, we present a new approach to find multiple concepts of DD by using an supervised mean shift algorithm. Unlike classic mean shift (an unsupervised clustering algorithm), our approach for the first time introduces the class label to feature point and each point differently contributes the mean shift iterations according to its label and position. A feature point derives from an MIL instance and takes corresponding bag label. Our supervised mean shift starts from positive points and converges to the local maxima that are close to the positive points and far away from the negative points. Experiments qualitatively indicate that our approach has better properties than other DD methods. © 2010 IEEE.
Feng, J-Y, Wang, M-C, Wang, C & Cao, L-B 1970, 'Enhanced co-occurrence distances for categorical data in unsupervised learning', 2010 International Conference on Machine Learning and Cybernetics, 2010 International Conference on Machine Learning and Cybernetics (ICMLC), IEEE, Qingdao, pp. 2071-2078.
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Distance metrics for categorical data play an important role in unsupervised learning such as clustering. They also dramatically affect learning accuracy and computational complexities. Recently, two co-occurrence methods, Co-occurrence Distance based on
Fookes, C, Denman, S, Lakemond, R, Ryan, D, Sridharan, S & Piccardi, M 1970, 'Semi-supervised intelligent surveillance system for secure environments', 2010 IEEE International Symposium on Industrial Electronics, 2010 IEEE International Symposium on Industrial Electronics (ISIE 2010), IEEE, Bari, Italy, pp. 2815-2820.
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This paper proposes a semi-supervised intelligent visual surveillance system to exploit the information from multi-camera networks for the monitoring of people and vehicles. Modules are proposed to perform critical surveillance tasks including: the manag
He, X, Wei, D, Lam, K-M, Li, J, Wang, L, Jia, W & Wu, Q 1970, 'Canny Edge Detection Using Bilateral Filter on Real Hexagonal Structure', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Advanced Concepts for Intelligent Vision System, Springer Berlin Heidelberg, Sydney, Australia, pp. 233-244.
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Edge detection plays an important role in image processing area. This paper presents a Canny edge detection method based on bilateral filtering which achieves better performance than single Gaussian filtering. In this form of filtering, both spatial closeness and intensity similarity of pixels are considered in order to preserve important visual cues provided by edges and reduce the sharpness of transitions in intensity values as well. In addition, the edge detection method proposed in this paper is achieved on sampled images represented on a real hexagonal structure. Due to the compact and circular nature of the hexagonal lattice, a better quality edge map is obtained on the hexagonal structure than common edge detection on square structure. Experimental results using proposed methods exhibit also the faster speed of detection on hexagonal structure. © 2010 Springer-Verlag.
Hijikata, Y & Xu, G 1970, 'SNSMW 2010 Workshop Organizers’ Message', Database Systems For Advanced Applications, 15th International Conference on DASFAA 2010, Springer Berlin Heidelberg, Tsukuba, JAPAN, pp. 239-239.
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Hussain, F 1970, 'Track 5 - business ecosystems', 4th IEEE International Conference on Digital Ecosystems and Technologies, 2010 4th IEEE International Conference on Digital Ecosystems and Technologies (DEST), IEEE.
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Janjua, NK & Hussain, FK 1970, 'Development of a Logic Layer in the Semantic Web: Research Issues', 2010 Sixth International Conference on Semantics, Knowledge and Grids, 2010 Sixth International Conference on Semantics Knowledge and Grid (SKG), IEEE, pp. 367-370.
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The ontology layer of the semantic web is now mature enough (i.e. standards like RDF, RDFs, OWL, OWL 2) and the next step is to work on a logic layer for the development of advanced reasoning capabilities for knowledge extraction and efficient decision making. Adding logic to the web means using rules to make inferences. Rules are a means of expressing business processes, policies, contracts etc but most of the studies have focused on the use of monotonic logics in layered development of the semantic web which provides no mechanism for representing or handling incomplete or contradictory information respectively. This paper discusses argumentation, semantic web and defeasible logic programming with their distinct features and identifies the different research issues that need to be addressed in order to realize defeasible argumentative reasoning in the semantic web applications. © 2010 IEEE.
Jia, W, He, X & Wu, Q 1970, 'ECCH: A novel color coocurrence histogram', 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, Dallas, USA, pp. 1258-1261.
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In this paper, a novel color cooccurrence histogram method, named eCCH which stands for color cooccurrence histogram at edge points, is proposed to describe the spatial-color joint distribution of images. Unlike all existing ideas, we only investigate the color distribution of pixels located at the two sides of edge points on gradient direction lines. When measuring the similarity of two eCCHs, the Gaussian weighted histogram intersection method is adopted, where both identical and similar color pairs are considered to compensate color variations. Comparative experimental results demonstrate the performance of the proposed eCCH in terms of robustness to color variance and small computational complexity. ©2010 IEEE.
Jia, W, He, X & Wu, Q 1970, 'Segmenting Characters from License Plate Images with Little Prior Knowledge', 2010 International Conference on Digital Image Computing: Techniques and Applications, 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE, Sydney, Australia, pp. 220-226.
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In this paper, to enable a fast and robust system for automatically recognizing license plates with various appearances, new and simple but efficient algorithms are developed to segment characters from extracted license plate images. Our goal is to segment characters properly from a license plate image region. Different from existing methods for segmenting degraded machine-printed characters, our algorithms are based on very weak assumptions and use no prior knowledge about the format of the plates, in order for them to be applicable to wider applications. Experimental results demonstrate promising efficiency and flexibility of the proposed scheme. © 2010 IEEE.
Juszczyszyn, K, Musial, A, Musial, K & Brodka, P 1970, 'Utilizing Dynamic Molecular Modelling Technique for Predicting Changes in Complex Social Networks', 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT), IEEE, pp. 1-4.
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We present a method that utilises dynamic molecular modelling technique to track the changes within complex social network. The users forming a social network are interpreted as large sets of interacting particles. The data for the conducted research was obtained from e-mail communication within Enron company. The social network of employees was extracted and used to evaluate the methodology of social network dynamics modelling. © 2010 IEEE.
Kadlec, P & Gabrys, B 1970, 'Adaptive on-line prediction soft sensing without historical data', The 2010 International Joint Conference on Neural Networks (IJCNN), 2010 International Joint Conference on Neural Networks (IJCNN), IEEE.
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Current soft sensing algorithms assume the availability of a large amount of training data. The collection of the historical data often takes a lot of time and can be expensive. At the same time not being able to provide sufficient amount of training data can result in sacrificing the performance of the soft sensor. This can be problematic in situations, where a soft sensor is urgently required and, at the same time, there is not enough training data available. This situation can occur, for example, when a new plant is taken into operation or, more critically, when there is a significant change in some parameters (e.g. operating point or the input materials) in a running plant. To deal with such a situation, we propose an algorithm, called Recursive Soft Sensing Algorithm (ReSSA), which delivers predictions without any explicit training phase. The proposed algorithm is based on the recursive functionality of the RPLS technique, which is embedded into local learning framework. More than that, during the run-time of the algorithm, it is not necessary to store any past data as the algorithm requires only the latest data point for its operation and recursive adaptation. In order to demonstrate the performance of the proposed method, it is applied to the prediction of a catalyst activity in a multi-tube reactor. © 2010 IEEE.
Kazienko, P, Brodka, P & Musial, K 1970, 'Individual Neighbourhood Exploration in Complex Multi-layered Social Network', 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT), IEEE, pp. 5-8.
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Social networks can be extracted from different data about communication or common activities in organizations, companies or various Internet-based services. Different types of data processed may result in creation of separate layers in the complex multi-layered social network. Analysis of neighbourhoods of network members and their utilization to social group discovery appears to be an interesting and important research domain. Since there is no measure to evaluate structure of the neighbourhoods in the multi-layered social network, a new measure called cross layered multi-layered clustering coefficient (CLMCC) is proposed in the paper. It enables to analyse the density of mutual connections of neighbours that occur in at least a given number of layers in a social network. Additionally, experimental studies on real-world data are presented. © 2010 IEEE.
Kazienko, P, Brodka, P, Musial, K & Gaworecki, J 1970, 'Multi-Layered Social Network Creation Based on Bibliographic Data', 2010 IEEE Second International Conference on Social Computing, 2010 IEEE Second International Conference on Social Computing (SocialCom), IEEE, pp. 407-412.
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A method for extraction of the multi-layered social network based on the data about human collaborative achievements, in particular scientific papers, is presented in the paper. The objects linking people form a hierarchy, which is flattened in the pre-processing stage. Only one level of the hierarchy remains together with new activities moved from its other levels. Separate layers of the multi-layered social network are created based on these pre-processed activities. © 2010 IEEE.
Khan, A, Zhang, J & Wang, Y 1970, 'Appearance-Based Re-identification of People in Video', 2010 International Conference on Digital Image Computing: Techniques and Applications, 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE, pp. 357-362.
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This paper introduces the topic of appearance-based reidentification of people in video. This work is based on colour information of people's clothing. Most of the work described in the literature uses full body histogram. This paper evaluates the histogram method and describes ways of including spatial colour information. The paper proposes a colour-based appearance descriptor called Colour Context People Descriptor. All the methods are evaluated extensively. The results are reported in the experiments. It is concluded at the end that adding spatial colour information greatly improves the re-identification results. © 2010 IEEE.
Korsunsky, AM, Hunter, A, Hukins, DWL, Gelman, L, Hogger, CJ, Ceglarek, DJ, Payne, S, Ao, SI, Ahmad, M, Alexandrou, I, Al-Nuaimy, W, Amavasai, BP, An, YY, Ariwa, E, Arteche, J, Audrino, F, Ayesh, A, Baber, C, Bailey, C, Balkan, N, Barria, J, Bartosova, J, Benkrid, K, Bleijs, H, Bluck, M, Bose, I, Bouzas, PR, Braiden, PM, Brdys, M, Burriesci, G, Cannataro, M, Carvalho, A, Chang, CC, Chen, D, Chen, GG, Chen, YS, Chiclana, F, Cooke, A, Das, DB, Davis, DN, Dayoub, I, Raman, SDCV, Demetriou, IC, Devai, F, Dilmaghani, RS, Dini, D, Drikakis, D, Durkan, C, Durodola, J, Etebar, K, Fenn, P, Figueiredo, A, Florou, G, Freear, S, Gabrys, B, Galbraith, GH, Gaskell, PH, Gaura, E, Ge, ZQ, Ghafouri-Shiraz, H, Ghavami, M, Giannopoulos, K, Pruneda Gonzalez, RE, Gracia, AM, Grecos, C, Guan, L, Gulpinar, N, Guo, R, Guo, Y, Hardalupas, Y, He, L, Herrero, JR, Hicks, BJ, Hines, EL, Hodgson, S, Horsfall, A, Hosein, P, Hu, F, Hu, O, Ijomah, W, Ming, J, James, A, Jancovic, P, Jhumka, A, Kamareddine, F, Kannan, R, Karsligil, ME, Katircioglu, ST, Khalid, A, Kokossis, A, Kontis, K, Kulekci, MO, Laukaitis, A, Leeson, M, Limbachiya, MC, Li, L, Li, L, Lin, P, Ling, WK & Macias Lopez, EM 1970, 'WCE 2010 - World Congress on Engineering 2010: Preface', WCE 2010 - World Congress on Engineering 2010.
Kusakunniran, W, Wu, Q, Zhang, J & Li, H 1970, 'Multi-view Gait Recognition Based on Motion Regression Using Multilayer Perceptron', 2010 20th International Conference on Pattern Recognition, 2010 20th International Conference on Pattern Recognition (ICPR), IEEE, Istanbul Turkey, pp. 2186-2189.
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It has been shown that gait is an efficient biometric feature for identifying a person at a distance. However, it is a challenging problem to obtain reliable gait feature when viewing angle changes because the body appearance can be different under the various viewing angles. In this paper, the problem above is formulated as a regression problem where a novel View Transformation Model (VTM) is constructed by adopting Multilayer Perceptron (MLP) as regression tool. It smoothly estimates gait feature under an unknown viewing angle based on motion information in a well selected Region of Interest (ROI) under other existing viewing angles. Thus, this proposal can normalize gait features under various viewing angles into a common viewing angle before gait similarity measurement is carried out. Encouraging experimental results have been obtained based on widely adopted benchmark database. © 2010 IEEE.
Kusakunniran, W, Wu, Q, Zhang, J & Li, H 1970, 'Support vector regression for multi-view gait recognition based on local motion feature selection', 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, San Francisco CA, USA, pp. 974-981.
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Gait is a well recognized biometric feature that is used to identify a human at a distance. However, in real environment, appearance changes of individuals due to viewing angle changes cause many difficulties for gait recognition. This paper re-formulates this problem as a regression problem. A novel solution is proposed to create a View Transformation Model (VTM) from the different point of view using Support Vector Regression (SVR). To facilitate the process of regression, a new method is proposed to seek local Region of Interest (ROI) under one viewing angle for predicting the corresponding motion information under another viewing angle. Thus, the well constructed VTM is able to transfer gait information under one viewing angle into another viewing angle. This proposal can achieve view-independent gait recognition. It normalizes gait features under various viewing angles into a common viewing angle before similarity measurement is carried out. The extensive experimental results based on widely adopted benchmark dataset demonstrate that the proposed algorithm can achieve significantly better performance than the existing methods in literature. ©2010 IEEE.
Lemke, C & Gabrys, B 1970, 'Meta-learning for time series forecasting in the NN GC1 competition', International Conference on Fuzzy Systems, 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE.
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There are no algorithms that generally perform better or worse than random when looking at all possible data sets according to the no-free-lunch theorem. A specific forecasting method will hence naturally have different performances in different empirical studies. This makes it impossible to draw general conclusions, however, there will of course be specific problems for which one algorithm performs better than another in practice. Meta-learning exploits this fact by linking characteristics of the data set to the performances of methods, adapting the selection or combination of base methods to a specific problem. This contribution describes an approach using meta-learning for time series forecasting in the NN GC1 competition. In order to generate bigger and more reliable meta-data set, data of the past NN3 and NN5 competitions have been included. A pool of individual forecasting and combination models are combined using a ranking algorithm with weights being determined by past performance on similar series. © 2010 IEEE.
Li, J, Liu, Q & Zeng, T 1970, 'Negative correlations in collaboration', Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10: The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Washington DC, pp. 463-472.
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Li, Z, Zhang, J, Wu, Q & Geers, G 1970, 'Feature Enhancement Using Gradient Salience on Thermal Image', 2010 International Conference on Digital Image Computing: Techniques and Applications, 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE, Sydney, Australia, pp. 556-562.
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Feature enhancement in an image is to reinforce some exacted features so that it can be used for object classification and detection. As the thermal image is lack of texture and colorful information, the techniques for visual image feature enhancement is insufficient to apply to thermal images. In this paper, we propose a new gradient-based approach for feature enhancement in thermal image. We use the statistical properties of gradient of foreground object profiles, and formulate object features with gradient saliency. Empirical evaluation of the proposed approach shows significant performance improved on human contours which can be used for detection and classification. © 2010 IEEE.
Liang, G & Zhang, C 1970, 'Empirical study of bagging predictors on medical data', Conferences in Research and Practice in Information Technology Series, Australian Data Mining Conference, ACS, Ballarat, Australia, pp. 31-40.
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This study investigates the performance of bagging in terms of learning from imbalanced medical data. It is important for data miners to achieve highly accurate prediction models, and this is especially true for imbalanced medical applications. In these situations, practitioners are more interested in the minority class than the majority class; however, it is hard for a traditional supervised learning algorithm to achieve a highly accurate prediction on the minority class, even though it might achieve better results according to the most commonly used evaluation metric, Accuracy. Bagging is a simple yet effective ensemble method which has been applied to many real-world applications. However, some questions have not been well answered, e.g., whether bagging outperforms single learners on medical data-sets; which learners are the best predictors for each medical data-set; and what is the best predictive performance achievable for each medical data-set when we apply sampling techniques. We perform an extensive empirical study on the performance of 12 learning algorithms on 8 medical data-sets based on four performance measures: True Positive Rate (TPR), True Negative Rate (TNR), Geometric Mean (G-mean) of the accuracy rate of the majority class and the minority class, and Accuracy as evaluation metrics. In addition, the statistical analyses performed instil confidence in the validity of the conclusions of this research. © 2011, Australian Computer Society, Inc.
Liu, B, Xiao, Y, Cao, L & Yu, PS 1970, 'Orientation distance-based discriminative feature extraction for multi-class classification', Proceedings of the 19th ACM international conference on Information and knowledge management, CIKM '10: International Conference on Information and Knowledge Management, ACM, Toronto, Ontario, Canada, pp. 909-918.
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Feature extraction is an effective step in data mining and machine learning. While many feature extraction methods have been proposed for clustering, classification and regression, very limited work has been done on multi-class classification problems. In fact, the accuracy of multi-class classification problems relies on well-extracted features, the modeling part aside. This paper proposes a new feature extraction method, namely extracting orientation distance-based discriminative (ODD) features, which is particularly designed for multi-class classification problems. The proposed method works in two steps. In the first step, we extend the Fisher Discriminant idea to determine more appropriate kernel function and map the input data with all classes into a feature space. In the second step, the ODD features are extracted based on the one-vs-all scheme to generate discriminative features between a pattern and each hyper-plane. These newly extracted features are treated as the representative features and are further used in the subsequent classification procedure. Substantial experiments on both UCI and real-world datasets have been conducted to investigate the performance of ODD features based multi-class classification. The statistical results show that the classification accuracy based on ODD features outperforms that of the state-of-the-art feature extraction methods. © 2010 ACM.
Liu, B, Xiao, Y, Cao, L & Yu, PS 1970, 'Vote-Based LELC for Positive and Unlabeled Textual Data Streams', 2010 IEEE International Conference on Data Mining Workshops, 2010 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, Sydney, NSW, Australia, pp. 951-958.
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In this paper, we extend LELC (PU Learning by Extracting Likely Positive and Negative Micro-Clusters) method to cope with positive and unlabeled data streams. Our developed approach, which is called vote-based LELC, works in three steps. In the first step, we extract representative documents from unlabeled data and assign a vote score to each document. The assigned vote score reflects the degree of belongingness of an example towards its corresponding class. In the second step, the extracted representative examples, together with their vote scores, are incorporated into a learning phase to build an SVM-based classifier. In the third step, we propose the usage of an ensemble classifier to cope with concept drift involved in the textual data stream environment. Our developed approach aims at improving the performance of LELC by rendering examples to contribute differently to the construction of the classifier according to their vote scores. Extensive experiments on textual data streams have demonstrated that vote-based LELC outperforms the original LELC method. © 2010 IEEE.
Liu, B, Yin, J, Xiao, Y, Cao, L & Yu, PS 1970, 'Exploiting Local Data Uncertainty to Boost Global Outlier Detection', 2010 IEEE International Conference on Data Mining, 2010 IEEE 10th International Conference on Data Mining (ICDM), IEEE, Sydney, pp. 304-313.
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This paper presents a novel hybrid approach to outlier detection by incorporating local data uncertainty into the construction of a global classifier. To deal with local data uncertainty, we introduce a confidence value to each data example in the training data, which measures the strength of the corresponding class label. Our proposed method works in two steps. Firstly, we generate a pseudo training dataset by computing a confidence value of each input example on its class label. We present two different mechanisms: kernel k-means clustering algorithm and kernel LOF-based algorithm, to compute the confidence values based on the local data behavior. Secondly, we construct a global classifier for outlier detection by generalizing the SVDD-based learning framework to incorporate both positive and negative examples as well as their associated confidence values. By integrating local and global outlier detection, our proposed method explicitly handles the uncertainty of the input data and enhances the ability of SVDD in reducing the sensitivity to noise. Extensive experiments on real life datasets demonstrate that our proposed method can achieve a better tradeoff between detection rate and false alarm rate as compared to four state-of-the-art outlier detection algorithms. © 2010 IEEE.
Meng, H-D, Ma, J-H & Xu, G-D 1970, 'Experimental Research on Impacts of Dimensionality on Clustering Algorithms', 2010 International Conference on Computational Intelligence and Software Engineering, 2010 International Conference on Computational Intelligence and Software Engineering (CiSE), IEEE.
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Experiments are carried out on datasets with different dimensions selected from UCI datasets by using two classical clustering algorithms. The results of the experiments indicate that when the dimensionality of the real dataset is less than or equal to 30, the clustering algorithms based on distance are effective. For high-dimensional datasets - dimensionality is greater than 30, the clustering algorithms are of weaknesses, even if we use dimension reduction methods, such as Principal Component Analysis (PCA). ©2010 IEEE.
Merigo, JM 1970, 'A METHOD FOR DECISION MAKING BASED ON PROBABILISTIC INFORMATION AND DISTANCE MEASURES', EDULEARN10: INTERNATIONAL CONFERENCE ON EDUCATION AND NEW LEARNING TECHNOLOGIES, 2nd International Conference on Education and New Learning Technologies (EDULEARN), IATED-INT ASSOC TECHNOLOGY EDUCATION & DEVELOPMENT, Barcelona, SPAIN.
Merigo, JM 1970, 'A METHOD FOR LINGUISTIC DECISION MAKING IN EDUCATIONAL MANAGEMENT', EDULEARN10: INTERNATIONAL CONFERENCE ON EDUCATION AND NEW LEARNING TECHNOLOGIES, 2nd International Conference on Education and New Learning Technologies (EDULEARN), IATED-INT ASSOC TECHNOLOGY EDUCATION & DEVELOPMENT, Barcelona, SPAIN.
Merigo, JM 1970, 'Fuzzy generalized aggregation operators in a unified model between the probability, the weighted average and the OWA operator', International Conference on Fuzzy Systems, 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Barcelona, SPAIN.
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Merigó, JM 1970, 'A generalized model between the OWA operator, the weighted average and the probability', Proceedings of the 2010 Spring Simulation Multiconference, SpringSim '10: 2010 Spring Simulation Conference, Society for Computer Simulation International.
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We introduce a new model that unifies the probability, the weighted average and the OWA operator in a general framework based on the use of generalized means. We present the generalized probabilistic ordered weighted averaging weighted averaging (GPOWAWA) operator. The main advantage of this model is that it unifies these three concepts considering the degree of importance that each one has in the aggregation. We study some of its main properties and particular cases such as the POWAWA, the quadratic POWAWA, the generalized probabilistic weighted average, the generalized OWAWA and generalized probabilistic OWA operator. We end the paper presenting a further generalization by using quasi-arithmetic means obtaining the Quasi-POWAWA operator. © 2010 SCS.
MERIGÓ, JM 1970, 'INDUCED GENERALIZED PROBABILISTIC OWAWA OPERATOR', Computational Intelligence in Business and Economics, Proceedings of the MS'10 International Conference, WORLD SCIENTIFIC, Barcelona, SPAIN, pp. 73-82.
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Merigó, JM 1970, 'Using the probabilistic weighted average in decision making with distance measures', WCE 2010 - World Congress on Engineering 2010, World Congress on Engineering (WCE 2010), INT ASSOC ENGINEERS-IAENG, Imperial Coll London, London, UNITED KINGDOM, pp. 1-4.
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We develop a new decision making method based on distance measures that uses the probabilistic weighted averaging (PWA) operator. The PWA operator is an aggregation operator that unifies the weighted average and the probability in the same formulation and considering the degree of importance that each concept has in the aggregation. We introduce the probabilistic weighted averaging distance (PWAD) operator. It is a new aggregation operator that uses probabilities, weighted averages and distance measures. We study some of its main properties and particular cases such as the arithmetic weighted Hamming distance and the arithmetic probabilistic Hamming distance. We also develop an application in a decision making problem concerning the selection of investment strategies.
Merigo, JM & Casanovas, M 1970, 'A NEW DECISION MAKING METHOD BASED ON DISTANCE MEASURES AND ITS APPLICATION IN EDUCATIONAL MANAGEMENT', 4TH INTERNATIONAL TECHNOLOGY, EDUCATION AND DEVELOPMENT CONFERENCE (INTED 2010), 4th International Technology, Education and Development Conference (INTED), IATED-INT ASSOC TECHNOLOGY EDUCATION & DEVELOPMENT, Valencia, SPAIN, pp. 987-998.
Merigo, JM & Casanovas, M 1970, 'DEALING WITH UNCERTAIN INFORMATION IN THE INDUCED PROBABILISTIC OWA OPERATOR', INTELLIGENT DECISION MAKING SYSTEMS, VOL. 2, 4th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2009), WORLD SCIENTIFIC PUBL CO PTE LTD, BELGIUM, Hasselt Univ, Hasselt, pp. 607-612.
Merigo, JM & Casanovas, M 1970, 'FUZZY AGGREGATION OPERATORS AND ITS APPLICATION IN THE SELECTION OF PROFESSORS', 4TH INTERNATIONAL TECHNOLOGY, EDUCATION AND DEVELOPMENT CONFERENCE (INTED 2010), 4th International Technology, Education and Development Conference (INTED), IATED-INT ASSOC TECHNOLOGY EDUCATION & DEVELOPMENT, Valencia, SPAIN, pp. 975-986.
MERIGÓ, JM & CASANOVAS, M 1970, 'DECISION MAKING WITH THE GENERALIZED PROBABILISTIC WEIGHTED AVERAGING DISTANCE OPERATOR', Computational Intelligence in Business and Economics, Proceedings of the MS'10 International Conference, WORLD SCIENTIFIC, Barcelona, SPAIN, pp. 541-548.
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Merigó, JM & Casanovas, M 1970, 'The induced probabilistic OWA distance and its application in decision making', Proceedings of the 2010 Spring Simulation Multiconference - Emerging M and S Applications in Industry and Academia Symposium, EAIA, pp. 180-185.
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We present the induced probabilistic ordered weighted averaging distance (IPOWAD) operator. It is a new distance measure that uses probabilistic information and induced aggregation operators. Thus, this model is able to assess problems where we have some kind of objective information and the attitudinal character of the decisionmaker is very complex and can be assessed with orderinducing variables that represent this attitude. We study some of it main properties and a wide range of particular cases including the arithmetic probabilistic distance, the arithmetic induced OWAD, the probabilistic distance, the normalized probabilistic distance, the probabilistic OWAD and many others. We also develop an application of the IPOWAD in a decision-making model regarding investment selection. © 2010 Simulation Councils, Inc.
Merigó, JM & Casanovas, M 1970, 'The induced probabilistic OWA distance and its application in decision making', Proceedings of the 2010 Spring Simulation Multiconference, SpringSim '10: 2010 Spring Simulation Conference, Society for Computer Simulation International.
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We present the induced probabilistic ordered weighted averaging distance (IPOWAD) operator. It is a new distance measure that uses probabilistic information and induced aggregation operators. Thus, this model is able to assess problems where we have some kind of objective information and the attitudinal character of the decision-maker is very complex and can be assessed with order-inducing variables that represent this attitude. We study some of it main properties and a wide range of particular cases including the arithmetic probabilistic distance, the arithmetic induced OWAD, the probabilistic distance, the normalized probabilistic distance, the probabilistic OWAD and many others. We also develop an application of the IPOWAD in a decision-making model regarding investment selection. © 2010 SCS.
Merigo, JM & Engemann, KJ 1970, 'Probabilistic aggregation operators with the induced generalized OWA operator', International Conference on Fuzzy Systems, 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Barcelona, SPAIN.
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Merigó, JM & Engemann, KJ 1970, 'A unified model between the OWA operator and the weighted average in decision making with Dempster-Shafer theory', WCE 2010 - World Congress on Engineering 2010, World Congress on Engineering (WCE 2010), INT ASSOC ENGINEERS-IAENG, Imperial Coll London, London, UNITED KINGDOM, pp. 83-87.
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We present a new decision making model by using the Dempster-Shafer belief structure that uses probabilities, weighted averages and the ordered weighted averaging (OWA) operator. Thus, we are able to represent the decision making problem considering objective and subjective information and the attitudinal character of the decision maker. For doing so, we use the ordered weighted averaging - weighted average (OWAWA) operator. It is an aggregation operator that unifies the weighted average and the OWA in the same formulation. As a result, we form the belief structure - OWAWA (BS-OWAWA) aggregation. We study some of its main properties and particular cases. We also present an application of the new approach in a decision making problem concerning political management.
MERIGÓ, JM & ENGEMANN, KJ 1970, 'FUZZY DECISION MAKING WITH PROBABILITIES AND INDUCED AGGREGATION OPERATORS', Computational Intelligence in Business and Economics, Proceedings of the MS'10 International Conference, WORLD SCIENTIFIC, Barcelona, SPAIN, pp. 323-332.
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MERIGÓ, JM & GIL-LAFUENTE, AM 1970, 'DECISION MAKING TECHNIQUES IN A UNIFIED MODEL BETWEEN THE WEIGHTED AVERAGE AND THE OWA OPERATOR', Computational Intelligence in Business and Economics, Proceedings of the MS'10 International Conference, WORLD SCIENTIFIC, Barcelona, SPAIN, pp. 181-188.
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MERIGÓ, JM & GIL-LAFUENTE, AM 1970, 'THE INDUCED GENERALIZED OWAWA DISTANCE OPERATOR', Computational Intelligence in Business and Economics, Proceedings of the MS'10 International Conference, WORLD SCIENTIFIC, Barcelona, SPAIN, pp. 11-18.
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Moemeng, C, Zhu, X & Cao, L 1970, 'Integrating Workflow into Agent-Based Distributed Data Mining Systems', Springer Berlin Heidelberg, Germany, pp. 4-15.
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Agent-based workflow has been proven its potential in overcoming issues in traditional workflow-based systems, such as decentralization, organizational issues, etc. The existing data mining tools provide workflow metaphor for data mining process visualization, audition and monitoring; these are particularly useful for distributed environments. In agent-based distributed data mining (ADDM), agents are an integral part of the system and can seamlessly incorporate with workflows. We describe a mechanism to use workflow in descriptive and executable styles to incorporate between workflow generators and executors. This paper shows that agent-based workflows can improve ADDM interoperability and flexibility, and also demonstrates the concepts and implementation with a supporting the argument, a multi-agent architecture and an agent-based workflow model are demonstrated.
Moemeng, C, Zhu, X, Cao, L & Jiahang, C 1970, 'i-Analyst: An Agent-Based Distributed Data Mining Platform', 2010 IEEE International Conference on Data Mining Workshops, 2010 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, Sydney, NSW, pp. 1404-1406.
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User-friendliness and performance are important properties of data mining and analysis tools. In this demo, we introduced an agent-based distributed data mining platform that allows users to manage and share the data-mining-related resources conveniently. Furthermore, the platform employs agents for workflow enactment in which the performance is improved with agent abilities. We also present an example to illustrate how the platform works in distributed environment. The performance is relatively competitive with non-agent approach when data is highly distributed and large.
Moghaddam, Z & Piccardi, M 1970, 'Histogram-Based Training Initialisation of Hidden Markov Models for Human Action Recognition', 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, Boston, Massachusetts, USA, pp. 256-261.
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Human action recognition is often addressed by use of latent-state models such as the hidden Markov model and similar graphical models. As such models require Expectation-Maximisation training, arbitrary choices must be made for training initialisation, with major impact on the final recognition accuracy. In this paper, we propose a histogram-based deterministic initialisation and compare it with both random and a time-based deterministic initialisations. Experiments on a human action dataset show that the accuracy of the proposed method proved higher than that of the other tested methods.
Moghaddam, Z & Piccardi, M 1970, 'Human action recognition with MPEG-7 descriptors and architectures', Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams, MM '10: ACM Multimedia Conference, ACM, Florence, Italy, pp. 63-68.
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Modern video surveillance requires addressing high-level concepts such as humans' actions and activities. In addition, surveillance applications need to be portable over a variety of platforms, from servers to mobile devices. In this paper, we explore the potential of the MPEG-7 standard to provide interfaces, descriptors, and architectures for human action recognition from surveillance cameras. Two novel MPEG-7 descriptors, symbolic and feature-based, are presented alongside two different architectures, server-intensive and client-intensive. The descriptors and architectures are evaluated in the paper by way of a scenario analysis.
Otoom, AF, Concha, OP & Piccardi, M 1970, 'Mixtures of Gaussian distributions under linear dimensionality reduction', VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications, VISAPP, Institute for Systems and Technologies of Information, Control and Communication, Angers, France, pp. 511-518.
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High dimensional spaces pose a serious challenge to the learning process. It is a combination of limited number of samples and high dimensions that positions many problems under the 'curse of dimensionality', which restricts severely the practical application of density estimation. 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 present a mixture model for reducing dimensionality based on a linear transformation which is not restricted to be orthogonal. Two methods are proposed for the learning of all the transformations and mixture parameters: the first method is based on an iterative maximum-likelihood approach and the second is based on random transformations and fixed (non iterative) probability functions. For experimental validation, we have used the proposed model for maximum-likelihood classification of five 'hard' data sets including data sets from the UCI repository and the authors' own. Moreover, we compared the classification performance of the proposed method with that of other popular classifiers including the mixture of Probabilistic Principal Component Analyzers and the Gaussian mixture model. In all cases but one, the accuracy achieved by the proposed method proved the highest, with increases with respect to the runner-up ranging from 0.2% to 5.2%.
Paisitkriangkrai, S, Mei, T, Zhang, J & Hua, X-S 1970, 'Scalable clip-based near-duplicate video detection with ordinal measure', Proceedings of the ACM International Conference on Image and Video Retrieval, CIVR' 10: International Conference on Image and Video Retrieval, ACM, Xi'an, pp. 121-128.
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Detection of duplicate or near-duplicate videos on large-scale database plays an important role in video search. In this paper, we analyze the problem of near-duplicates detection and propose a practical and effective solution for real-time large-scale v
Paisitkriangkrai, S, Shen, C & Zhang, J 1970, 'Face Detection with Effective Feature Extraction', Computer Vision ACCV 2010, Asian Conference on Computer Vision, SpringerLink, Queenstown, New Zealand, pp. 460-470.
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There is an abundant literature on face detection due to its important rolein many vision applications. Since Viola and Jones proposed the first real-timeAdaBoost based face detector, Haar-like features have been adopted as themethod of choice for frontal face detection. In this work, we show that simplefeatures other than Haar-like features can also be applied for training aneffective face detector. Since, single feature is not discriminative enough toseparate faces from difficult non-faces, we further improve the generalizationperformance of our simple features by introducing feature co-occurrences. Wedemonstrate that our proposed features yield a performance improvement comparedto Haar-like features. In addition, our findings indicate that features play acrucial role in the ability of the system to generalize.
Pan, R, Xu, G & Dolog, P 1970, 'User and document group approach of clustering in tagging systems', Proceedings ABIS 2010 - 18th Intl. Workshop on Personalization and Recommendation on the Web and Beyond.
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In this paper, we propose a spectral clustering approach for users and documents group modeling in order to capture the common preference and relatedness of users and documents, and to reduce the time complexity of similarity calculations. In experiments, we investigate the selection of the optimal amount of clusters. We also show a reduction of the time consuming in calculating the similarity for the recommender systems by selecting a centroid first, and then compare the inside item on behalf of each group. keywords: User Profile, Document Profile, Spectral Clustering, Group Profile, Modularity Metric.
Parvin, S, Han, S, Gao, L, Hussain, F & Chang, E 1970, 'Towards Trust Establishment for Spectrum Selection in Cognitive Radio Networks', 2010 24th IEEE International Conference on Advanced Information Networking and Applications, 2010 24th IEEE International Conference on Advanced Information Networking and Applications, IEEE, pp. 579-583.
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Cognitive Radio (CR) has been considered as a promising concept for improving the utilization of limited radio spectrum resources for future wireless communications and mobile computing. As cognitive radio network (CRN) is a general wireless heterogeneous network, it is very essential for detecting the misbehaving or false nodes in the network. So in this paper we propose a trust aware model which provides a reliable approach to establish trust for CRN. This approach combines all kinds of trust values together, including the direct trust and indirect trust value for the secondary users. Depending on this trust value, it is decided that whether the secondary user can user the primary user's spectrum band or not. The mathematical results show that our trust model can efficiently take decision for assigning spectrums to the users. © 2010 IEEE.
Parvin, S, Han, S, Tian, B & Hussain, FK 1970, 'Trust-Based Authentication for Secure Communication in Cognitive Radio Networks', 2010 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, 2010 IEEE/IFIP 8th International Conference on Embedded and Ubiquitous Computing (EUC) (Co-Located with CSE 2010), IEEE, pp. 589-596.
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Over the past few years, 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. 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 the other conventional wireless networks. This work thus proposes a secure trust-based authentication approach for CRNs. A CR node's trust value is determined from its previous trust behavior in the network and depending on this trust value, it is decided whether or not this CR node will obtain access to the Primary User's free spectrum. The security analysis is performed to guarantee that the proposed approach achieves security proof. © 2010 IEEE.
Raza, M, Hussain, FK, Hussain, OK & Chang, E 1970, 'MD2 METRICS FOR OPTIMIZING TRUST PREDICTION IN DIGITAL BUSINESS ECOSYSTEM', INTELLIGENT DECISION MAKING SYSTEMS, International Conference on Intelligent Systems and Knowledge Engineering, World Scientific And Engineering Acad And Soc, Hasselt, BELGIUM, pp. 402-410.
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The modelling and management of trust between interacting parties are crucial parts of the overall business intelligence strategy for any organization. Predicting trust values is a key element of modelling and managing trust. It is of critical importance when the interaction is to be conducted at a future point in time. In the existing body of work, there are a few approaches for predicting trust. However, none of these approaches proposes a framework or methodology by which the predicted trust value can be considered in light of its accuracy or confidence level. This is a key element in order to ensure optimized trust prediction. In this paper, we propose a methodology to address this critical issue. The methodology comprises a suite of metrics-maturity, distance and density (MD2) which are capable of capturing various aspects of the confidence level in the predicted trust value. The proposed methodology is exemplified with the help of case studies. © 2010 Springer Science+Business Media, LLC.
Roberts, D, Roberts, M, Liu, X, Roberts, J, Lipman, J & Bellomo, R 1970, 'CLEARANCE OF ANTIBIOTICS BY HIGH AND LOW INTENSITY CONTINUOUS RENAL REPLACEMENT THERAPY IN CRITICALLY ILL PATIENTS', NEPHROLOGY, WILEY-BLACKWELL, pp. 87-87.
Saesue, W, Chou, CT & Zhang, J 1970, 'CROSS-layer QoS-optimized EDCA adaptation for wireless video streaming', 2010 IEEE International Conference on Image Processing, 2010 17th IEEE International Conference on Image Processing (ICIP 2010), IEEE, Hong Kong, pp. 2925-2928.
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In this paper, we propose an adaptive cross layer technique that optimally enhance the QoS of wireless video transmission in an IEEE 802.11e WLAN. The optimization takes into account the unequal error protection characteristics of video streaming, the IE
Saesue, W, Chou, CT & Zhang, J 1970, 'Video quality prediction in the presence of MAC contention and wireless channel error', 2010 IEEE International Symposium on 'A World of Wireless, Mobile and Multimedia Networks' (WoWMoM), 2010 IEEE International Symposium on 'A World of Wireless, Mobile and Multimedia Networks' (WoWMoM), IEEE.
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This paper proposes an integrated model to predict the quality of video, expressed in terms of mean square error (MSE) of the received video frames, in an IEEE 802.11e wireless network. The proposed system takes into account contention at the MAC layer, wireless channel error, queueing at the MAC layer, parameters of different 802.11e access categories (ACs), and video characteristics of different H.264 data partitions (DPs). To the best of the authors' knowledge, this is the first system that takes these network and video characteristics into consideration to predict video quality in an IEEE 802.11e network. The proposed system consists of two components. The first component predicts the packet loss rate of each H.264 data partition by using a multi-dimensional discrete-time Markov chain (DTMC) coupled to a M/G/1 queue. The second component uses these packet loss rates and the video characteristics to predict the MSE of each received video frames. We verify the accuracy of our combination system by using discrete event simulation and real H.264 coded video sequences. ©2010 IEEE.
Saesue, W, Zhang, J & Chou, CT 1970, 'Frame-recursive block-based distortion estimation model for multiple reference frames and motion copy concealment in H.264/AVC', 2010 18th International Packet Video Workshop, 2010 18th International Packet Video Workshop (PV), IEEE.
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Su, H, Chen, L, Ye, Y, Sun, Z & Wu, Q 1970, 'A Refinement Approach to Handling Model Misfit in Semi-supervised Learning', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Berlin Heidelberg, Chongqing, China, pp. 75-86.
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Semi-supervised learning has been the focus of machine learning and data mining research in the past few years. Various algorithms and techniques have been proposed, from generative models to graph-based algorithms. In this work, we focus on the Cluster-and-Label approaches for semi-supervised classification. Existing cluster-and-label algorithms are based on some underlying models and/or assumptions. When the data fits the model well, the classification accuracy will be high. Otherwise, the accuracy will be low. In this paper, we propose a refinement approach to address the model misfit problem in semi-supervised classification. We show that we do not need to change the cluster-and-label technique itself to make it more flexible. Instead, we propose to use successive refinement clustering of the dataset to correct the model misfit. A series of experiments on UCI benchmarking data sets have shown that the proposed approach outperforms existing cluster-and-label algorithms, as well as traditional semi-supervised classification techniques including Selftraining and Tri-training. © 2010 Springer-Verlag.
Tang, M, Wang, W, Jiang, Y, Zhou, Y, Li, J, Cui, P, Liu, Y & Yan, B 1970, 'Birds Bring Flues? Mining Frequent and High Weighted Cliques from Birds Migration Networks', DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, PT II, PROCEEDINGS, 15th International Conference on Database Systems for Advanced Applications, Springer Berlin Heidelberg, Tsukuba, JAPAN, pp. 359-369.
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Thi, TH, Cheng, L, Zhang, J & Wang, L 1970, 'Implicit Motion-Shape Model: A generic approach for action matching', 2010 IEEE International Conference on Image Processing, 2010 17th IEEE International Conference on Image Processing (ICIP 2010), IEEE, Hong Kong, pp. 1477-1480.
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We develop a robust technique to find similar matches of human actions in video. Given a query video, Motion History Images (MHI) are constructed for consecutive keyframes. This is followed by dividing the MHI into local Motion-Shape regions, which allow
Thi, TH, Cheng, L, Zhang, J, Wang, L & Satoh, S 1970, 'Weakly Supervised Action Recognition Using Implicit Shape Models', 2010 20th International Conference on Pattern Recognition, 2010 20th International Conference on Pattern Recognition (ICPR), IEEE, Istanbul, pp. 3517-3520.
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In this paper, we present a robust framework for action recognition in video, that is able to perform competitively against the state-of-the-art methods, yet does not rely on sophisticated background subtraction preprocess to remove background features.
Thi, TH, Zhang, J, Cheng, L, Wang, L & Satoh, S 1970, 'Human Action Recognition and Localization in Video Using Structured Learning of Local Space-Time Features', 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, Boston, MA, pp. 204-211.
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This paper presents a unified framework for human action classification and localization in video using structured learning of local space-time features. Each human action class is represented by a set of its own compact set of local patches. In our approach, we first use a discriminative hierarchical Bayesian classifier to select those space-time interest points that are constructive for each particular action. Those concise local features are then passed to a Support Vector Machine with Principal Component Analysis projection for the classification task. Meanwhile, the action localization is done using Dynamic Conditional Random Fields developed to incorporate the spatial and temporal structure constraints of superpixels extracted around those features. Each superpixel in the video is defined by the shape and motion information of its corresponding feature region. Compelling results obtained from experiments on KTH [22], Weizmann [1], HOHA [13] and TRECVid [23] datasets have proven the efficiency and robustness of our framework for the task of human action recognition and localization in video. © 2010 IEEE.
Wang, L, Cheng, L, Thi, TH & Zhang, J 1970, 'Human Action Recognition from Boosted Pose Estimation', 2010 International Conference on Digital Image Computing: Techniques and Applications, 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE, Sydney, NSW, pp. 308-313.
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This paper presents a unified framework for recognizing human action in video using human pose estimation. Due to high variation of human appearance and noisy context background, accurate human pose analysis is hard to achieve and rarely employed for the task of action recognition. In our approach, we take advantage of the current success of human detection and view invariability of local feature-based approach to design a pose-based action recognition system. We begin with a frame-wise human detection step to initialize the search space for human local parts, then integrate the detected parts into human kinematic structure using a tree structural graphical model. The final human articulation configuration is eventually used to infer the action class being performed based on each single part behavior and the overall structure variation. In our work, we also show that even with imprecise pose estimation, accurate action recognition can still be achieved based on informative clues from the overall pose part configuration. The promising results obtained from action recognition benchmark have proven our proposed framework is comparable to the existing state-of-the-art action recognition algorithms.
Wang, S, Du, R, Wu, Q & He, X 1970, 'Adaptive Stick-Like Features for Human Detection Based on Multi-scale Feature Fusion Scheme', 2010 International Conference on Digital Image Computing: Techniques and Applications, 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE, Sydney, Australia, pp. 375-380.
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Human detection has been widely used in many applications. In the meantime, it is still a difficult problem with many open questions due to challenges caused by various factors such as clothing, posture and etc. By investigating several benchmark methods and frameworks in the literature, this paper proposes a novel method which successfully implements the Real AdaBoost training procedure on multi-scale images. Various object features are exposed on multiple levels. To further boost the overall performance, a fusion scheme is established using scores obtained at various levels which integrates decision results with different scales to make the final decision. Unlike other score-based fusion methods, this paper re-formulates the fusion process through a supervised learning. Therefore, our fusion approach can better distinguish subtle difference between human objects and non-human objects. Furthermore, in our approach, we are able to use simpler weak features for boosting and hence alleviate the training complexity existed in most of AdaBoost training approaches. Encouraging results are obtained on a well recognized benchmark database. © 2010 IEEE.
Wang, W, Zhang, J & Shen, C 1970, 'Improved human detection and classification in thermal images', 2010 IEEE International Conference on Image Processing, 2010 17th IEEE International Conference on Image Processing (ICIP 2010), IEEE, Hong Kong, pp. 2313-2316.
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We present a new method for detecting pedestrians in thermal images. The method is based on the Shape Context Descriptor (SCD) with the Adaboost cascade classifier framework. Compared with standard optical images, thermal imaging cameras offer a clear advantage for night-time video surveillance. It is robust on the light changes in day-time. Experiments show that shape context features with boosting classification provide a significant improvement on human detection in thermal images. In this work, we have also compared our proposed method with rectangle features on the public dataset of thermal imagery. Results show that shape context features are much better than the conventional rectangular features on this task.
Xiao, Y, Liu, B, Cao, L, Yin, J & Wu, X 1970, 'SMILE: A Similarity-Based Approach for Multiple Instance Learning', 2010 IEEE International Conference on Data Mining, 2010 IEEE 10th International Conference on Data Mining (ICDM), IEEE, Sydney, NSW, Australia, pp. 589-598.
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Multiple instance learning (MIL) is a generalization of supervised learning which attempts to learn useful information from bags of instances. In MIL, the true labels of the instances in positive bags are not always available for training. This leads to a critical challenge, namely, handling the ambiguity of instance labels in positive bags. To address this issue, this paper proposes a novel MIL method named SMILE (Similarity-based Multiple Instance LEarning). It introduces a similarity weight to each instance in positive bag, which represents the instance similarity towards the positive and negative classes. The instances in positive bags, together with their similarity weights, are thereafter incorporated into the learning phase to build an extended SVM-based predictive classifier. Experiments on three real-world datasets consisting of 12 subsets show that SMILE achieves markedly better classification accuracy than state-of-the-art MIL methods. © 2010 IEEE.
Xu, G, Zong, Y, Dolog, P & Zhang, Y 1970, 'Co-clustering Analysis of Weblogs Using Bipartite Spectral Projection Approach', Knowledge-based And Intelligent Information And Engineering Systems, Pt Iii, 14th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, Springer Berlin Heidelberg, Cardiff, WALES, pp. 398-407.
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Web clustering is an approach for aggregating Web objects into various groups according to underlying relationships among them. Finding co-clusters of Web objects is an interesting topic in the context of Web usage mining, which is able to capture the un
Xu, M, Chen, L, He, X, Xu, C & Jin, JS 1970, 'Adaptive local hyperplanes for MTV affective analysis', Proceedings of the Second International Conference on Internet Multimedia Computing and Service, ICIMCS '10: The Second International Conference on Internet Multimedia Computing and Service, ACM, Harbin, China, pp. 167-170.
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Affective analysis attracts increasing attention in multimedia domain since affective factors directly reflect audiences' attention, evaluation and memory. Existing study focuses on mapping low-level affective features to high-level emotions by applying machine learning methods. Therefore, choosing effective features and developing efficient machine learning algorithms become vital for affective analysis. In this paper, we investigate the effectiveness of a novel classification approach, called Adaptive Local Hyperplanes (ALH), in affective analysis. The reason ALH is appealing in affective analysis is two-fold. Firstly, affective features are not equally important for emotion categories; ALH inherently assigns feature weights based on discriminative ability of each feature. Secondly, ALH achieves competitive performance with state-of-the-art classifiers (e.g., SVM) while it is designed for multi-class classification. Consequently, it is worthwhile to explore the usage of ALH in affective analysis. MTV data are used in this study. As the first effort of applying ALH to affective analysis, the results presented in this paper provide a foundation for future research in affective analysis. Copyright 2010 ACM.
Yang, T, Cao, L & Zhang, C 1970, 'A Novel Prototype Reduction Method for the K-Nearest Neighbor Algorithm with K ≥ 1', Advances in Knowledge Discovery and Data Mining - Lecture Notes in Artificial Intelligence, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer Berlin Heidelberg, Hyderabad, India, pp. 89-100.
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In this paper, a novel prototype reduction algorithm is proposed, which aims at reducing the storage requirement and enhancing the online speed while retaining the same level of accuracy for a K-nearest neighbor (KNN) classifier. To achieve this goal, our proposed algorithm learns the weighted similarity function for a KNN classifier by maximizing the leave-one-out cross-validation accuracy. Unlike the classical methods PW, LPD and WDNN which can only work with K>=1, our developed algorithm can work with K>=1. This flexibility allows our learning algorithm to have superior classification accuracy and noise robustness. The proposed approach is assessed through experiments with twenty real world benchmark data sets. In all these experiments, the proposed approach shows it can dramatically reduce the storage requirement and online time for KNN while having equal or better accuracy than KNN, and it also shows comparable results to several prototype reduction methods proposed in literature.
Yang, T, Kecman, V, Cao, L & Zhang, C 1970, 'Combining Support Vector Machines and the t-statistic for Gene Selection in DNA Microarray Data Analysis', Advances in Knowledge Discovery and Data Mining - Lecture Notes in Artificial Intelligence, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer Berlin Heidelberg, Hyderabad, India, pp. 55-62.
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This paper proposes a new gene selection (or feature selection) method for DNA microarray data analysis. In the method, the t-statistic and support vector machines are combined efficiently. The resulting gene selection method uses both the data intrinsic information and learning algorithm performance to measure the relevance of a gene in a DNA microarray. We explain why and how the proposed method works well. The experimental results on two benchmarking microarray data sets show that the proposed method is competitive with previous methods. The proposed method can also be used for other feature selection problems.
Yang, T, Vojislav, K, Longbing, C & Chengqi, Z 1970, 'Testing Adaptive Local Hyperplane for multi-class classification by double cross-validation', The 2010 International Joint Conference on Neural Networks (IJCNN), 2010 International Joint Conference on Neural Networks (IJCNN), IEEE, Barcelona, Spain, pp. 1-5.
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Adaptive Local Hyperplane (ALH) is a recently proposed classifier for the multi-class classification problems and it has shown encouraging performance in many pattern recognition problems. However, ALH's performance over many general classification datasets has only been tested by using a single loop of cross-validation procedure, where the whole datasets are used for both hyper-parameter determination and accuracy estimation. This procedure is appropriate for classifier performance comparison, but the produced results are likely to be optimistic for classifier accuracy estimation on new datasets. In this paper, we test the performance of ALH as well as several other benchmark classifiers by using two loops of cross-validation (a.k.a. double resampling) procedure, where the inner loop is used for hyper-parameter determination and the outer loop is used for accuracy estimation. With such a testing scheme, the classification accuracy of a tested classifier can be evaluated in a more strict way. The experimental results indicate the superior performance of the ALH classifier with respect to the traditional classifiers including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), Classification Tree (Tree) and K-local Hyperplane distance Nearest Neighbor (HKNN). These results imply that the ALH classifier might become a useful tool for the pattern recognition tasks.
ZadJabbari, B, Wongthongtham, P, Hussain, FK & Soc, IEEEC 1970, 'Knowledge sharing effectiveness measurement', 2010 24TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION NETWORKING AND APPLICATIONS (AINA), pp. 1249-1254.
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Knowledge would be considered as important element in knowledge-based economy and it makes a strong competitive advantage in dynamic business environment. In knowledge management, knowledge sharing is the most critical elements of effective knowledge processing. Several studies have been done to explain why people share knowledge and some of them have been mentioned in this paper. The next issue is how knowledge sharing can be improved and how it can be measured. Different models from different view points such as social and psychological aspect or economic benefit aspect have been proposed to analyse and measure knowledge sharing effectiveness. In this paper, we will review some of the main models in knowledge sharing effectiveness and will explain a new method to measure knowledge sharing effectiveness among individuals. © 2010 IEEE.
Zhang, C 1970, 'Welcome Message from the Conference Chairs', 2010 IEEE International Conference on Data Mining, 2010 IEEE 10th International Conference on Data Mining (ICDM), IEEE.
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Zhang, C & Gunopulos, D 1970, 'Welcome Message from the Conference Chairs', 2010 IEEE International Conference on Data Mining Workshops, 2010 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE.
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Zhang, J, Shen, C & Geers, G 1970, 'Preface', 2010 International Conference on Digital Image Computing: Techniques and Applications, 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE.
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Zhao, Y, Bohlscheid, H, Wu, S & Cao, L 1970, 'Less Effort, More Outcomes: Optimising Debt Recovery with Decision Trees', 2010 IEEE International Conference on Data Mining Workshops, 2010 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, Sydney, NSW, Australia, pp. 655-660.
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This paper presents a real-world application of data mining techniques to optimise debt recovery in social security. The traditional method of contacting a customer for the purpose of putting in place a debt recovery schedule has been an out-bound phone call, and by and large, customers are chosen at random. This obsolete and inefficient method of selecting customers for debt recovery purposes has existed for years and in order to improve this process, decision trees were built to model debt recovery and predict the response of customers if contacted by phone. Test results on historical data show that, the built model is effective to rank customers in their likelihood of entering into a successful debt recovery repayment schedule. If contacting the top 20 per cent of customers in debt, instead of contacting all of them, approximately 50 per cent of repayments would be received.
Zheng, Z, Zhao, Y, Zuo, Z & Cao, L 1970, 'An Efficient GA-Based Algorithm for Mining Negative Sequential Patterns.', PAKDD (1), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Hyderabad, India, pp. 262-273.
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Negative sequential pattern mining has attracted increasing concerns in recent datamining research because it considers negative relationships between itemsets, which are ignored by positive sequential pattern mining. However, the search space for mining negative patterns is much bigger than that for positive ones.When the support threshold is low, in particular, there will be huge amounts of negative candidates. This paper proposes a Genetic Algorithm (GA) based algorithm to find negative sequential patterns with novel crossover and mutation operations, which are efficient at passing good genes on to next generations without generating candidates. An effective dynamic fitness function and a pruning method are also provided to improve performance. The results of extensive experiments show that the proposed method can find negative patterns efficiently and has remarkable performance compared with some other algorithms of negative pattern mining.
Zhu, L & Li, J 1970, 'Water Bioinformatics: An Association between Estrogen Degradation and 16S rRNA Motifs', 2010 4th International Conference on Bioinformatics and Biomedical Engineering, 2010 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE), IEEE.
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The existence of estrogenic compounds in the water severely pollutes the ecological environment. It is believed that microorganisms such as harmless bacterium can be used as a clean and safe medium to naturally degrade the estrogens. Many bacteria have been found to be capable of degrading estrogens in different ways and speeds. While the degradation mechanism, in particular, the association between the degradation capability and their phylogenetic motifs is unknown yet. In this paper, we analyzed the 16S rRNA gene sequences of 17 kinds of bacteria, which are usually used for phylogenetic studies. We examined the association between motifs and degradation by distinguishing such motifs that could separate those bacteria into several similar functional groups. Our computational result shows that the motifs have a various positive associations to the degradation, implying that different biodegradation factors are in the play. © 2010 IEEE.
Zong, Y, Xu, G, Dolog, P, Zhang, Y & Liu, R 1970, 'Co-clustering for Weblogs in Semantic Space', Web Information System Engineering-wise 2010, 11th International Conference on Web Information Systems Engineering, Springer Berlin Heidelberg, Hong Kong, PEOPLES R CHINA, pp. 120-127.
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Web clustering is an approach for aggregating web objects into various groups according to underlying relationships among them. Finding co-clusters of web objects in semantic space is an interesting topic in the context of web usage mining, which is able
