Creating ensembles of classifiers via fuzzy clustering and deflection

Ensembles of classifiers can increase the performance of pattern recognition, and have become a hot research topic. High classification accuracy and diversity of the component classifiers are essential to obtain good generalization capability of an ensemble. We review the methods used to learn diverse classifiers, employ fuzzy clustering with deflection to learn the distribution characteristics of the training data, and propose a novel sampling approach to generate training data sets for the component classifiers. Our approach increases the classification accuracy and diversity of the component classifiers. The approach is evaluated using the base classifier c4.5, and the experimental results show that it outperforms Bagging and AdaBoost on almost all the randomly selected 20 benchmark UCI data sets.