Using Accuracy and Diversity to Select Classifiers to Build Ensembles
Rodrigo G. F. Soares, Alixandre Thiago Ferreira de Santana, Anne M. P. Canuto, Marcílio C. P. de Souto · The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006
Ensemble of classifiers is an effective way of improving performance of individual classifiers. However, the task of selecting the ensemble members is often a non-trivial one. For example, in some cases, a bad selection strategy could lead to ensembles with no performance improvement. Thus, many researchers have put a lot of effort in finding an effective method for selecting classifier for building ensembles. In this context, a dynamic classifier selection (DCS) method is proposed, which takes into account both the accuracy and the diversity of the classifiers.