Improving the Mann–Whitney statistical test for feature selection: An approach in breast cancer diagnosis on mammography
作者：
Highlights：
• An innovative feature selection method (named uFilter) is proposed.
• A set of image-based features, from mammography lesions, were explored and successfully ranked.
• Classification's performance of four different machine learning algorithms increased in almost all scenarios when using the uFilter method.
• The uFilter method statistically improved the breast cancer classification in mammography.
• The efficiency of the uFilter method was confirmed by the Wilcoxon statistical test.
摘要
•An innovative feature selection method (named uFilter) is proposed.•A set of image-based features, from mammography lesions, were explored and successfully ranked.•Classification's performance of four different machine learning algorithms increased in almost all scenarios when using the uFilter method.•The uFilter method statistically improved the breast cancer classification in mammography.•The efficiency of the uFilter method was confirmed by the Wilcoxon statistical test.
论文关键词：Feature selection methods,Mann–Whitney U-test,uFilter method,Machine learning algorithms,Redundancy analysis,Breast cancer CADx
论文评审过程：Received 24 January 2014, Revised 21 November 2014, Accepted 4 December 2014, Available online 12 December 2014.
论文官网地址：https://doi.org/10.1016/j.artmed.2014.12.004
