Many real world classification problems have highly imbalanced and skew data distributions. In fault diagnosis and condition monitoring for example, there are ample data for the normal class, yet data for faults are always very limited and costly to obtain. It is often a challenge to increase the performance of a classifier on the minority classes without sacrificing the performance on the majority classes. This talk discusses some of the techniques and algorithms that have been developed for class imbalance learning, especially through ensemble learning. First, the motivations behind ensemble learning are introduced and the importance of diversity highlighted. Second, some of the challenges of multi-class imbalance learning and potential solutions are presented. What might have worked well for the binary case do not work for multiple classes anymore, especially when the number of classes increases. Third, online class imbalance learning will be discussed, which can be seen as a combination of online learning and class imbalance learning. Online class imbalance learning poses new research challenges that still have not been well understood., let alone solved, epecially for imbalanced data streams with concept drift. Fourth, the natural fit of multi-objective learning to class imbalance learning is mentioned. The relationship between multi-objective learning and ensemble learning will be discussed. Finally, future research diections will be pointed out.

Biography: Xin Yao is a Chair Professor of Computer Science at the Southern University of Science and Technology, Shenzhen, China, and a part-time Professor of Computer Science at the University of Birmingham, UK. His major research interests include evolutionary computation, ensemble learning and search-based software engineering. His work won the 2001 IEEE Donald G. Fink Prize Paper Award; 2010, 2015 and 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Awards; 2010 BT Gordon Radley Award for Best Author of Innovation (Finalist); 2011 IEEE Transactions on Neural Networks Outstanding Paper Award; and many other best paper awards. He received a prestigious Royal Society Wolfson Research Merit Award in 2012 and the IEEE CIS Evolutionary Computation Pioneer Award in 2013. He was ecently selected to receive the 2020 IEEE Frank Rosenblatt Award. More information can be found at his home page.