Title: Improving Mutual Information based Feature Selection by Boosting Unique Relevance. Abstract: Mutual Information (MI) based feature selection makes use of MI to evaluate each feature and eventually shortlist a relevant feature subset, in order to address issues associated with high-dimensional datasets.
Despite the effectiveness of MI in feature selection, we have noticed that many state-of-the-art algorithms disregard the so-called unique relevance (UR) of features, which is a necessary condition for the optimal feature subset. In fact, in our study of seven state-of-the-art and classical MIBFS algorithms, we find that all of them underperform as they ignore UR of features and arrive at a suboptimal selected feature subset which contains a non-negligible number of redundant features. We point out that the heart of the problem is that all these MIBFS algorithms follow the criterion of Maximize Relevance with Minimum Redundancy (MRwMR), which does not explicitly target UR. This motivates us to augment the existing criterion with the objective of boosting unique relevance (BUR), leading to a new criterion called MRwMR-BUR. We conduct extensive experiments with several MIBFS algorithms with and without incorporating UR. The results indicate that the algorithms that boost UR consistently outperform their unboosted counterparts in terms of peak accuracy and number of features required. Furthermore, we propose a classifier based approach to estimate UR that further improves the performance of MRwMR-BUR based algorithms.