Clustering and selection of neural networks using adaptive differential evolution
Tiago Pessoa Lima, Adenilton José da Silva, Teresa B. Ludermir · 2012
This paper explores the automatic construction of multiple classifiers systems using the selection method. The automatic method proposed is composed by two phases: one for designing the individual classifiers and one for clustering patterns of training set and search specialized classifiers for each cluster found. The performed experiments adopted the artificial neural networks in the classification phase and k-means in clustering phase. Adaptive differential evolution has been used in this work in order to optimize the parameters and performance of the different 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 using the fusion version for a set of ?? benchmark problems.