Feature selection in heterogeneous structure of ensembles: A genetic algorithm approach

Classifier ensembles are systems composed of a set of individual classifiers (organized 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. However, the majority of the papers using feature selection for ensembles address the homogenous structures of ensemble, i.e., ensembles composed only of the same type of classifiers. In this paper, two approaches of genetic algorithms (single and multi-objective) will be used to guide the distribution of the features among the classifiers in the context of heterogeneous ensembles.