Logistic local hyperplane-Relief: A feature weighting method for classification

作者：

Highlights：

• This paper proposes LLH-Relief based on LH-Relief and LI-Relief.

• LLH-Relief uses local learning to find neighbor representations for given samples.

• LLH-Relief solves a problem with l1-norm to obtain sparse feature weights.

• Experimental results show the good feature selection ability of LLH-Relief.

摘要

•This paper proposes LLH-Relief based on LH-Relief and LI-Relief.•LLH-Relief uses local learning to find neighbor representations for given samples.•LLH-Relief solves a problem with l1-norm to obtain sparse feature weights.•Experimental results show the good feature selection ability of LLH-Relief.

论文关键词：Feature selection,Relief,Local learning,l1-norm regularization,Logistic regression

论文评审过程：Received 23 October 2018, Revised 6 March 2019, Accepted 16 April 2019, Available online 3 June 2019, Version of Record 16 August 2019.

论文官网地址：https://doi.org/10.1016/j.knosys.2019.04.011