Keynote Talk: Data, Machine Learning and Decision-Making

The talk will present how machine learning can innovatively and effectively learn from big data to support data-driven decision-making in uncertain and dynamic situations. A set of new fuzzy transfer learning theories, methodologies and algorithms are proposed that transfer knowledge learnt in one or more source domains to target domains by building latent space and mapping functions to overcome tremendous uncertainties in data, learning processes and decision outputs (classification and regression). Another set of concept drift theories, methodologies and algorithms are developed to handle ever-changing dynamic data stream environments with unpredictable stream pattern drifts by effectively and accurately detecting concept drift in an explanatory way, indicating when, where and how concept drift occurs and reacting accordingly. These new developments enable smart learning and therefore enhance data-driven prediction, recommendation and decision support systems in uncertain and dynamic environments.