A genetic encoding approach for learning methods for combining classifiers

Several studies have reported that the ensemble of classifiers can improve the performance of a stand-alone classifier. In this paper, we propose a learning method for combining the predictions of a set of classifiers. The method described in this paper uses a Genetic-based version of the Correspondence Analysis for combining classifiers. The Correspondence Analysis is based on the orthonormal representation of the labels assigned to the patterns by a pool of classifiers. In this paper instead of the orthonormal representation we use a pool of representations obtained by a Genetic Algorithm. Each single representation is used to train a different classifiers, these classifiers are combined by vote rule. The performance improvement with respect to other learning-based fusion methods is validated through experiments with several benchmark datasets.