A stable feature selection method based on relevancy and redundancy

Citations Over Time

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.

Related Papers

• Efficient Feature Selection via Analysis of Relevance and Redundancy(2004)
• → On some aspects of minimum redundancy maximum relevance feature selection(2019)69 cited
• → A stable feature selection method based on relevancy and redundancy(2021)4 cited
• → Hopfield Networks in Relevance and Redundancy Feature Selection Applied to Classification of Biomedical High-Resolution Micro-CT Images(2008)3 cited
• Feature Selection Based on Minimal Redundancy Principle for Text Classification(2007)