ReinSel: A class-based mechanism for feature selection in ensemble of classifiers

Classifier ensembles are systems composed of a set of individual classifiers structured in a parallel way and a combination module, which is responsible for providing the final output of the system. In the design of these systems, 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 provide different datasets (patterns and/or attributes) for the individual classifiers. In this context, it is envisaged to use, for instance, feature selection methods in order to select subsets of attributes for the individual classifiers. In this paper, it is investigated the ReinSel method, which is a class-based feature selection method for ensemble systems. This method is inserted into the filter approach of feature selection methods and it chooses only the attributes that are important only for a specific class through the use of a reinforcement procedure.

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► In this work, we present a class-based feature selection method for ensemble system.
► This method is inserted into the filter approach of feature selection methods.
► This method chooses only attributes that are important only for a specific class.
► It uses a reinforcement two-step procedure to choose the important attributes.
► The proposed method was comparatively analyzed with a standard filter-based method.