Using a reinforcement-based feature selection method in Classifier Ensemble

In the design of Classifier Ensembles, diversity is considered as one of the main aspects to be taken into account, since there is no gain in combining identical classification methods. One way of increasing diversity is to use feature selection methods in order to select subsets of attributes for the individual classifiers. In this paper, it is investigated the use of a simple reinforcement-based method, called ReinSel, in ensemble systems. More specifically, it is aimed to evaluate the capability of this method to select the correct attributes of a dataset, avoiding unimportant and noisy attributes.