A Feature Selection Algorithm Based on Equal Interval Division and Conditional Mutual Information

• Published in: Neural processing letters Vol. 54; no. 3; pp. 2079 - 2105
• Main Authors: Gu, Xiangyuan, Guo, Jichang, Ming, Tao, Xiao, Lijun, Li, Chongyi
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2022; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Codes; Complex Systems; Computational Intelligence; Computer Science; Effectiveness; Feature selection; Information theory; Lattice theory; Variables; Equal interval division; Feature selection; Conditional mutual information; Classification
• ISSN: 1370-4621; 1573-773X
• DOI: 10.1007/s11063-021-10720-6

The performance of many feature selection algorithms is affected because of ignoring three-dimensional mutual information among features. Three-dimensional mutual information includes conditional mutual information, joint mutual information and three-way interaction information. Aiming at the limitation, this paper investigates feature selection based on three-dimensional mutual information. First, we propose an objective function based on conditional mutual information. Further, we propose a criterion to validate whether the objective function can guarantee the effectiveness of selected features. In the case that the objective function cannot guarantee the effectiveness of selected features, we combine a method of equal interval division and ranking with the objective function to select features. Finally, we propose a feature selection algorithm named EID-CMI. To validate the performance of EID-CMI, we compare it with several feature selection algorithms. Experimental results demonstrate that EID-CMI can achieve better feature selection performance.