Unsupervised feature selection for ensemble of classifiers

In this paper we discuss a strategy to create ensemble of classifiers based on unsupervised features selection. It takes into account a hierarchical multi-objective genetic algorithm that generates a set of classifiers by performing feature selection and then combines them to provide a set of powerful ensembles. The proposed method is evaluated in the context of handwritten month word recognition, using three different feature sets and hidden Markov models as classifiers. Comprehensive experiments demonstrate the effectiveness of the proposed strategy.