A stable feature selection method based on relevancy and redundancy. Issue 1 (January 2021)

• Record Type: Journal Article
• Authors: Shen, Peng; Ding, Xiaoming; Ren, Wenjun; Liu, Shu
• Is Part Of: Journal of physics. Volume 1732:Issue 1(2021)
• Publication Date: 2021-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1732/1/012023
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In this paper, the characteristics of software defect prediction are analyzed from the perspective of machine learning. To solve the problem of some redundant or uncorrelated features in defect data sets, a stable feature selection method based on relevancy and redundancy (RRSFS) is proposed. RRSFS combines the redundancy between features and the correlation between features and classes to select the optimal subset. RRSFS not only reduces the cost of data operation in the prediction model, but also enhances the stability of feature selection algorithm.