Empirical comparison of Dynamic Classifier Selection methods based on diversity and accuracy for building ensembles

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.