Multi-objective genetic algorithm evaluation in feature selection

Abstract

Feature Selection may be viewed as a search for optimal feature subsets considering one or more importance criteria. This search may be performed with Multi-objective Genetic Algorithms. In this work, we present an application of these algorithms for combining different filter approach criteria, which rely on general characteristics of the data, as feature-class correlation, to perform the search for subsets of features. We conducted experiments on public data sets and the results show the potential of this proposal when compared to mono-objective genetic algorithms and two popular filter algorithms. © 2011 Springer-Verlag.