k-best feature selection and ranking via stochastic approximation
作者：
Highlights：
• We propose k-best feature selection and ranking based on stochastic approximation.
• This method involves simultaneous random perturbation of feature weights.
• We use Barzilai & Borwein non-monotone gains with gradient averaging and gain smoothing.
• We present experiments with four classifiers and four regressors on various datasets.
• Over 80% of classification and regression experiments were equivalent or better.
摘要
•We propose k-best feature selection and ranking based on stochastic approximation.•This method involves simultaneous random perturbation of feature weights.•We use Barzilai & Borwein non-monotone gains with gradient averaging and gain smoothing.•We present experiments with four classifiers and four regressors on various datasets.•Over 80% of classification and regression experiments were equivalent or better.
论文关键词：Explainable artificial intelligence,Feature selection,Feature ranking,Stochastic approximation,Barzilai and Borwein method
论文评审过程：Received 20 December 2021, Revised 16 September 2022, Accepted 16 September 2022, Available online 22 September 2022, Version of Record 29 September 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118864
