Evolving classifiers ensembles with heterogeneous predictors

Abstract

XCS with computed prediction, namely XCSF, extends XCS by replacing the classifier prediction with a parametrized prediction function. Although several types of prediction functions have been introduced, so far XCSF models are still limited to evolving classifiers with the same prediction function. In this paper, we introduce XCSF with heterogeneous predictors, XCSFHP, which allows the evolution of classifiers with different types of prediction function within the same population. We compared XCSFHP to XCSF on several problems. Our results suggest that XCSFHP generally performs as XCSF with the most appropriate prediction function for the given problem. In particular, XCSFHP seems able to evolve, in each problem subspace, the most adequate type of prediction function. © 2008 Springer Berlin Heidelberg.