Class-specific mutual information variation for feature selection

摘要

Feature selection plays a critical role in pattern recognition. Feature selection aims to eliminate irrelevant and redundant features. A drawback of traditional feature selection methods is that they ignore the dynamic change of selected features with the class. To address this problem, we develop a novel linear feature selection method, namely, Dynamic Change of Selected Feature with the class (DCSF). In DCSF, we introduce a new term: the conditional mutual information between the selected features and the class when a candidate feature is considered. In addition, we replace the traditional feature relevancy term with a term that is based on conditional mutual information. To evaluate our method, we compare DCSF with five traditional methods and two state-of-the-art methods on 20 benchmark data sets. Experimental results show that DCSF outperforms seven other methods in terms of average classification accuracy and highest classification accuracy. (C) 2018 Elsevier Ltd. All rights reserved.