Cooperative coevolutionary ensemble learning

Abstract

A new optimization technique is proposed for classifier fusion - Cooperative revolutionary Ensemble Learning (CCEL). It is based on a specific multipopulational evolutionary algorithm - cooperative coevolution. It can be used as a wrapper over any kind of weak algorithms, learning procedures and fusion functions, for both classification and regression tasks. Experiments on the real-world problems from the UCI repository show that CCEL has a fairly high generalization performance and generates ensembles of much smaller size than boosting, bagging and random subspace method. © Springer-Verlag Berlin Heidelberg 2007.