Uncertainty Management in Machine Learning and its Applications in Food Data Analytics

Project Member(s): Zhou, J., Li, Z., Chen, F.

Funding or Partner Organisation: Food Agility CRC Limited (Food Agility CRC)
Food Agility CRC Limited (Food Agility CRC)

Start year: 2021

Summary: The proposed research will concentrate on uncertainty management in machine learning and the application in food data analytics. Inputs will introduce uncertainty in machine learning models. The uncertainty will be included in the model in two ways, from noisy observation or an insufficient number of observations. Uncertainty management plays an important in machine learning tasks because it can provide a framework to quantify the uncertainty and produce well-performing models with optimization tricks under uncertain conditions.

Publications:

Weeraddana, D, MallawaArachchi, S, Warnakula, T, Li, Z & Wang, Y 1970, 'Long-Term Pipeline Failure Prediction Using Nonparametric Survival Analysis', Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Springer International Publishing, Belgium, Online, pp. 139-156.
View/Download from: Publisher's site

FOR Codes: Kiwifruit, Pattern Recognition and Data Mining, Knowledge Representation and Machine Learning, Machine learning not elsewhere classified

Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.