Clustering and Selection Using Grouping Genetic Algorithms for Blockmodeling to Construct Neural Network Ensembles

The choice of a Committee of Classifiers is based on the idea that two or more classifiers can make a better decision than a single one. In the literature there are several methodologies for construction of committees and among them Classifier Selection that determines the best or a subset with the most efficient classifiers in each region of the feature space. Blockmodeling is a useful tool for describing the fundamental structure of social networks, but it is used in this work in a non-social data. As shown in the literature, clustering data and then choosing a classifier for each cluster can increase the committee performance and Evolutionary Algorithms can increase even more the performance. Thus this paper proposes BMGGAVS using a combination of Blockmodeling and Genetic Algorithms in order to cluster data and through a simple vote system assign a Neural Network for each cluster. Results from experiments in 9 databases indicate that BMGGAVS is able to obtain a good performance.