An improvement direction for filter selection techniques using information theory measures and quadratic optimization
Machine Learning
2012-08-21 v1 Information Theory
math.IT
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.
Cite
@article{arxiv.1208.3689,
title = {An improvement direction for filter selection techniques using information theory measures and quadratic optimization},
author = {Waad Bouaguel and Ghazi Bel Mufti},
journal= {arXiv preprint arXiv:1208.3689},
year = {2012}
}
Comments
4 pages, 2 tables, (IJARAI) International Journal of Advanced Research in Artificial Intelligence