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By Professor Xin Yao, Chair of Computer Science, University of Birmingham, UK, and President of IEEE Computational Intelligence Society.
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 as 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 i.e., 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 provided to gain a better understanding of how and when diversity may help ensemble learning.
Towards the end of the talk, a new learning framework - learning in the model space will be introduced.
Professor Xin Yao, Chair (Professor) of Computer Science at the University of Birmingham, UK, and President of IEEE Computational Intelligence Society