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

An improvement direction for filter selection techniques using information theory measures and quadratic optimization