Professor Xin Yao,
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Previous work on evolving neural networks has focused on single neural networks. However, monolithic neural networks have become too complex to trainand evolve for large and complex problems. It is often better to design acollection of simpler neural networks that work collectively and cooperatively to solve a large and complex problem. The key issue here is how to design such a collection, i.e., an ensemble, automatically so that it has the best generalisation ability. This talk first reviews briefly early work on evolving neural networks. Then a previous idea of designing ensembles, negative correlation learning, is explained. Lastly, several recent studies are introduced, which analyze the impact of diversity on online ensemble learning and that on multi-class class imbalance learning. The ideas behind some new ensemble algorithms for online learning, class imbalance learning, and online class imbalance learning will be presented. Applications of such new ensemble learning algorithms will also be mentioned and future research directions discussed.
Xin Yao is a Chair (Professor) of Computer Science and the Director of CERCIA (Centre of Excellence for Research in Computational Intelligence and Applications) at the University of Birmingham, UK. He is an IEEE Fellow and the President (2014-15) of IEEE Computational Intelligence Society (CIS).He 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.He won the prestigious Royal Society Wolfson Research Merit Award in 2012 and the IEEE CIS Evolutionary Computation Pioneer Award in 2013. He was the Editor-in-Chief (2003-08) of IEEE Transactions on Evolutionary Computation and is an Associate Editor or Editorial Member of more than ten other journals. He has been invited to give 70+ keynote/plenary speeches at international conferences. His major research interests include evolutionary computation and neural network ensembles.