An analysis of diversity measures for the dynamic design of ensemble of classifiers

Researches with ensemble Systems have emerged as an attempt to obtain a computational system that works with classification tasks in an efficient way. The main goal of using ensemble systems is to improve the performance of a pattern recognition system in terms of better generalization and/or of clearer design. One of the main challenges in the design of a ensemble system is the definition of the system components. The choice of the ensemble members can become a very difficult task and, in some cases, it can lead to ensembles with no performance improvement. In order to avoid this situation, the idea of DES (Dynamic Ensemble Selection)-based method has emerged, in which the classifiers to compose the ensemble systems are chosen in a dynamic way. In this paper, we present an analysis of different diversity measures in two dynamic ensemble election methods. These two methods use accuracy and diversity as the main criteria to select classifiers dynamically. The goal of this paper is to investigate the influence of different diversity measure in the dynamic selection of classifiers.