Class-specific mutual information variation for feature selection. (July 2018)

• Record Type: Journal Article
• Authors: Gao, Wanfu; Hu, Liang; Zhang, Ping
• Is Part Of: Pattern recognition. Volume 79(2018:Jul.)
• Page Start: 328
• Page End: 339
• Publication Date: 2018-07
• Subjects: Feature selection -- Information theory -- Dynamic change -- Classification; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.02.020
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A novel feature selection method is proposed based on information theory. A new term calculating dynamic information of selected features is proposed. We redefine the feature relevancy. We implement experiments on 20 benchmark data sets. Our method outperforms seven competing methods in terms of accuracy. Abstract: 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.