Genetic algorithms with diversity measures to build classifier systems

The combination of classifiers is an active research area of the machine learning and pattern recognition communities. Many theoretical and empirical studies have been published demonstrating the advantages of the paradigm of combination of classifiers over the individual classifiers. When combining classifiers it is important to guarantee the diversity among them [16]. Some statistical measures can be used to estimate how diverse the ensembles of classifiers are, they are called diversity measures.