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

Authors:
Anne M. P. Canuto;Karliane M. O. Vale;Antonino Feitos;Alberto Signoretti
Affiliations:
Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Campus Universitario, Lagoa Nova, Natal - RN 59072-970, Brazil;Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Campus Universitario, Lagoa Nova, Natal - RN 59072-970, Brazil;Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Campus Universitario, Lagoa Nova, Natal - RN 59072-970, Brazil;Computing and Automation Engineering Department, UFRN, Natal, Brazil
Venue:
Applied Soft Computing
Year:
2012

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Abstract

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.