Feature selection using Joint Mutual Information Maximisation
作者：
Highlights：
• Two new feature selection methods are proposed based on joint mutual information.
• The methods use joint mutual information with maximum of the minimum criterion.
• The methods address the problem of selection of redundant and irrelevant features.
• The methods are evaluated using eleven public data sets and five competing methods.
• The proposed JMIM method outperforms five competing methods in terms of accuracy.
摘要
•Two new feature selection methods are proposed based on joint mutual information.•The methods use joint mutual information with maximum of the minimum criterion.•The methods address the problem of selection of redundant and irrelevant features.•The methods are evaluated using eleven public data sets and five competing methods.•The proposed JMIM method outperforms five competing methods in terms of accuracy.
论文关键词：Feature selection,Mutual information,Joint mutual information,Conditional mutual information,Subset feature selection,Classification,Dimensionality reduction,Feature selection stability
论文评审过程：Received 21 October 2014, Revised 1 July 2015, Accepted 4 July 2015, Available online 19 July 2015, Version of Record 29 August 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.07.007
