Informative variable identifier: Expanding interpretability in feature selection

作者：

Highlights：

• Interpretability of the solution is provided by a novel feature selection algorithm.

• Relevant, redundant and non-informative input variables are identified.

• Analysis of weights learned by resampling allows to clarify relations among variables.

• Improvement in the interpretability of the results and in classification performance.

摘要

•Interpretability of the solution is provided by a novel feature selection algorithm.•Relevant, redundant and non-informative input variables are identified.•Analysis of weights learned by resampling allows to clarify relations among variables.•Improvement in the interpretability of the results and in classification performance.

论文关键词：Feature selection,Interpretability,Explainable machine learning,Resampling,Classification

论文评审过程：Received 28 June 2019, Revised 4 September 2019, Accepted 7 October 2019, Available online 8 October 2019, Version of Record 14 October 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.107077