Optimization techniques for the selection of members and attributes in ensemble systems

Although ensemble systems have been proved to be efficient for pattern recognition tasks, its elaboration and design is not an easy task. Some aspects such as the choice of its individual classifiers and the use of feature selection methods are very difficult to define. In addition, these aspects can have a strong effect in the accuracy of these systems, leading, for instance, to cases where the produced ensembles have no performance improvement. In order to avoid this situation, there is a great deal of research to select individual classifiers or distribute attributes to the individual classifiers of ensemble systems. In most of these works, however, only one aspect is tackled (either member selection or feature selection). In this paper, we present an analysis of two well-known optimization techniques to choose the ensemble members and to select attributes for these individual classifiers. In order to do this analysis, we use accuracy as well as two recently proposed diversity measures as parameters, in a multi-objective optimization problem.