When classifier selection meets information theory: A unifying view

Classifier selection aims to reduce the size of an ensemble of classifiers in order to improve its efficiency and classification accuracy. Recently an information-theoretic view was presented for feature selection. It derives a space of possible selection criteria and show that several feature selection criteria in the literature are points within this continuous space. The contribution of this paper is to export this information-theoretic view to solve an open issue in ensemble learning which is classifier selection. We investigated a couple of information-theoretic selection criteria that are used to rank classifiers.