| Title | Ensemble Approaches in Learning |
| Speaker | Prof. Xin Yao |
| Chair | Ke Tang |
Abstract
Designing a monolithic system for a large and complex learning task is hard. Divide-and-conquer is a common strategy in tackling such large and complex problems. Ensembles can be regarded an automatic approach towards automatic divide-and-conquer. Many ensemble methods, including boosting, bagging, negative correlation, etc., have been used in machine learning and data mining for many years. This talk will describe three examples of ensemble methods in multi-objective learning, online learning with concept drift, and multi-class imbalance learning. Given the important role of diversity in ensemble methods, some discussions and analysis will be given to gain a better understanding of how and when diversity may help ensemble learning.
Some materials used in the talk were based on the following papers: A Chandra and X. Yao, “Ensemble learning using multi-objective evolutionary algorithms”, Journal of Mathematical modeling and Algorithms, 5(4):417-445, December 2006.
L. L. Minku and X. Yao, “DDD: A New Ensemble Approach For Dealing With Concept Drift,” IEEE Transactions on Knowledge and Data Engineering, 24(4):619-633, April 2012.
S. Wang and X. Yao, “Multi-Class Imbalance Problems: Analysis and Potential Solutions,” IEEE Transactions on Systems, Man and Cybernetics, Part B, 42(4):1119-1130, August 2012.
Biography
Xin Yao is a Chair (Professor) of Computer Science and the Director of CERCIA (the Centre of Excellence for Research in Computational Intelligence and Applications), University of Birmingham, UK. He is an IEEE Fellow and a Distinguished Lecturer of IEEE Computational Intelligence Society (CIS). His work won the 2001 IEEE Donald G. Fink Prize Paper Award, 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, 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 at conferences. He won the prestigious Royal Society Wolfson Research Merit Award in 2012 and was selected to receive the 2013 IEEE CIS Evolutionary Computation Pioneer Award. He was the Editor-in-Chief (2003-08) of IEEE Transactions on Evolutionary Computation. He has been invited to give more than 70 keynote/plenary speeches at international conferences.
His major research interests include evolutionary computation and ensemble learning. He has more than 400 refereed publications in international journals and conferences.
