Optimal ensemble construction via meta-evolutionary ensembles

Authors:
YongSeog Kim;W. Nick Street;Filippo Menczer
Affiliations:
Business Information Systems, Utah State University, Logan, UT 84322, USA;Management Sciences, University of Iowa, Iowa City, IA 52242, USA;School of Informatics, Indiana University, Bloomington, IN 47406, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2006

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Abstract

In this paper, we propose a meta-evolutionary approach to improve on the performance of individual classifiers. In the proposed system, individual classifiers evolve, competing to correctly classify test points, and are given extra rewards for getting difficult points right. Ensembles consisting of multiple classifiers also compete for member classifiers, and are rewarded based on their predictive performance. In this way we aim to build small-sized optimal ensembles rather than form large-sized ensembles of individually-optimized classifiers. Experimental results on 15 data sets suggest that our algorithms can generate ensembles that are more effective than single classifiers and traditional ensemble methods.