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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.

In the context of Ensembles or Multi-Classifier Systems, the choice of the ensemble members is a very complex task, in which, in some cases, it can lead to ensembles with no performance improvement. In order to avoid this situation, there is a great deal of research to find effective classifier member selection methods. In this paper, we propose a selection criterion based on both the accuracy and diversity of the classifiers in the initial pool. Also, instead of using a static selection method, we use a Dynamic Classifier Selection (DSC) procedure. In this case, the member classifiers to form the ensemble are chosen at the test (use) phase. That is, different testing patterns can be classified by different ensemble configurations.