A Dynamic Classifier Selection Method to Build Ensembles using Accuracy and Diversity

Authors:
Alixandre Santana;Rodrigo G. F. Soares;Anne M. P. Canuto;Marcilio C. P. de Souto
Affiliations:
Federal University of Rio Grande do Norte (UFRN), Brazil;Federal University of Rio Grande do Norte (UFRN), Brazil;Federal University of Rio Grande do Norte (UFRN), Brazil;Federal University of Rio Grande do Norte (UFRN), Brazil
Venue:
SBRN '06 Proceedings of the Ninth Brazilian Symposium on Neural Networks
Year:
2006

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Abstract

Ensemble of classifiers is an effective way of improving performance of individual classifiers. However, the choice of the ensemble members can become a very difficult task, in which, in some cases, it can lead to ensembles with no performance improvement. In order to avoid this situation, there is a need to find effective classifier member selection methods. In this paper, a DCS (Dynamic Classifier Selection)-based method is presented, which takes into account performance and diversity of the classifiers in order to choose the ensemble members.