A combination of fuzzy similarity measures and fuzzy entropy measures for supervised feature selection

作者：

Highlights：

• Combination of similarity and entropy measures (FSAE) for feature selection.

• Includes scale-factor for inter-class distances to scale entropy values.

• The model is tested on five medical data sets from UCI Machine Learning Depository.

• Feature selection algorithm results in high classification accuracy on given data sets.

摘要

•Combination of similarity and entropy measures (FSAE) for feature selection.•Includes scale-factor for inter-class distances to scale entropy values.•The model is tested on five medical data sets from UCI Machine Learning Depository.•Feature selection algorithm results in high classification accuracy on given data sets.

论文关键词：Feature ranking,Filter method,Wrapper method,Machine learning,ReliefF

论文评审过程：Received 13 October 2017, Revised 31 May 2018, Accepted 1 June 2018, Available online 3 June 2018, Version of Record 18 June 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.06.002