Use of multi-objective genetic algorithms to investigate the diversity/accuracy dilemma in heterogeneous ensembles

Authors:
Diogo F. de Oliveira;Anne M. P. Canuto;Marcilio C. P. de Souto
Affiliations:
Informatics and Applied Mathematics Department, Federal University of Rio Grande do Norte, Natal, RN, Brazil;Informatics and Applied Mathematics Department, Federal University of Rio Grande do Norte, Natal, RN, Brazil;Informatics and Applied Mathematics Department, Federal University of Rio Grande do Norte, Natal, RN, Brazil
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

Classifier ensembles, also known as committees, are systems composed of a set of base classifiers (organized in a paraIlel way) and a combination module, which is responsible for providing the final output of the system. The main aim of using ensembles is to provide better performance than the individual classifiers. In order to build robust ensembles, it is often required that the base classifiers are as accurate as diverse among themselves - this is known as thc diversity/accuracy dilemma. There are, in the literature, some works analyzing the ensemble performance in context of such a dilemma. However, the majority of them address the homogenous structures, i.e., ensembles composed only of the same type of classifiers. Motivated by such a limitation, this paper presents an empirical investigation on the diversity/accuracy dilemma for heterogeneous ensembles. In order to do so, multi-objective genetic algorithms will be used to guide the building of the ensemble systems.