  Filter selection techniques are known for their simplicity and efficiency.
However this kind of methods doesn't take into consideration the features
inter-redundancy. Consequently the un-removed redundant features remain in the
final classification model, giving lower generalization performance. In this
paper we propose to use a mathematical optimization method that reduces
inter-features redundancy and maximize relevance between each feature and the
target variable.
