A Comparative Analysis of Feature Selection Methods for Ensembles with Different Combination Methods

Feature selection methods are applied in ensembles in order to find subsets of features for the classifiers of the ensemble. The use of these methods aims to reduce the redundancy of the features as well as to increase diversity of the classifiers of an ensemble. In this paper, a comparative analysis of six different feature selection methods is performed in ensembles using six different combination methods. The main aim of this paper is to investigate which combination methods are more affected by the use of feature selection methods.