  From a machine learning point of view, identifying a subset of relevant
features from a real data set can be useful to improve the results achieved by
classification methods and to reduce their time and space complexity. To
achieve this goal, feature selection methods are usually employed. These
approaches assume that the data contains redundant or irrelevant attributes
that can be eliminated. In this work, we propose a novel algorithm to manage
the optimization problem that is at the foundation of the Mutual Information
feature selection methods. Furthermore, our novel approach is able to estimate
automatically the number of dimensions to retain. The quality of our method is
confirmed by the promising results achieved on standard real data sets.
