Selection and fusion of neural networks via differential evolution

Abstract

This paper explores the automatic construction of multiclassifiers systems based on a combination of selection and fusion. The method proposed is composed by two phases: one for designing the individual classifiers and one for clustering patterns of training set and search a set of classifiers for each cluster found. In our experiments, we adopted the artificial neural networks in the classification phase and selforganizing maps in clustering phase. Differential evolution with global and local neighborhoods has been used in this work in order to optimize the parameters and performance of the techniques used in classification and clustering phases. The experimental results have shown that the proposed method has better performance than manual methods and significantly outperforms most of the methods commonly used to combine multiple classifiers for a set of 4 benchmark problems. © Springer-Verlag Berlin Heidelberg 2012.