A new maximum relevance-minimum multicollinearity (MRmMC) method for feature selection and ranking

作者：

Highlights：

• A new maximum relevance–minimum multicollinearity (MRmMC) method is proposed.

• The proposed MRmRC algorithm was applied to a number of real-life datasets; experimental results are reported and compared with several state-of-the-art methods.

• Numerical analysis results confirmed the promising performance of the proposed method.

摘要

•A new maximum relevance–minimum multicollinearity (MRmMC) method is proposed.•The proposed MRmRC algorithm was applied to a number of real-life datasets; experimental results are reported and compared with several state-of-the-art methods.•Numerical analysis results confirmed the promising performance of the proposed method.

论文关键词：Dimensionality reduction,Feature selection,Classification,Correlation measure,Qualitative and quantitative variables

论文评审过程：Received 11 May 2016, Revised 9 December 2016, Accepted 18 January 2017, Available online 1 February 2017, Version of Record 10 February 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.01.026