High-order conditional mutual information maximization for dealing with high-order dependencies in feature selection
作者：
Highlights：
• Mutual information feature selection method based on conditional mutual information.
• Novel mutual information feature selection algorithm, exploring high order dependencies.
• The proposed method allows seeing the mutual information feature selection with highorder dependencies with clear interpretation.
• Compared with different methods over 20 benchmark datasets, the proposed method reached the best results.
• The proposed method is faster and accurate than other feature selection methods that explore high order dependencies.
摘要
•Mutual information feature selection method based on conditional mutual information.•Novel mutual information feature selection algorithm, exploring high order dependencies.•The proposed method allows seeing the mutual information feature selection with highorder dependencies with clear interpretation.•Compared with different methods over 20 benchmark datasets, the proposed method reached the best results.•The proposed method is faster and accurate than other feature selection methods that explore high order dependencies.
论文关键词：Feature selection,Mutual information,Information theory,Pattern recognition
论文评审过程：Received 4 December 2021, Revised 4 May 2022, Accepted 9 July 2022, Available online 10 July 2022, Version of Record 21 July 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.108895
