Abstract: 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.

Waad Bouaguel and Ghazi Bel Mufti, “An improvement direction for filter selection techniques using information theory measures and quadratic optimization” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 1(5), 2012. http://dx.doi.org/10.14569/IJARAI.2012.010502

@article{Bouaguel2012,
title = {An improvement direction for filter selection techniques using information theory measures and quadratic optimization},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2012.010502},
url = {http://dx.doi.org/10.14569/IJARAI.2012.010502},
year = {2012},
publisher = {The Science and Information Organization},
volume = {1},
number = {5},
author = {Waad Bouaguel and Ghazi Bel Mufti}
}