A Dynamic Classifier Selection Method to Build Ensembles using Accuracy and Diversity
Alixandre Thiago Ferreira de Santana, Rodrigo G. F. Soares, Anne M. P. Canuto, Marcílio C. P. de Souto · 2006
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.