An efficient henry gas solubility optimization for feature selection

作者：

Highlights：

• Henry gases solubility optimization is used for the first time for feature selection.

• The results revealed that HGSO shows high efficiency over the 12 datasets.

• The proposed method is compared with six well-known optimization algorithms.

• HGSO shows a high quality over a high accuracy and less number of selected features.

摘要

•Henry gases solubility optimization is used for the first time for feature selection.•The results revealed that HGSO shows high efficiency over the 12 datasets.•The proposed method is compared with six well-known optimization algorithms.•HGSO shows a high quality over a high accuracy and less number of selected features.

论文关键词：Classification,Dimensionality reduction,Feature selection (FS),Henry gas solubility optimization (HGSO),Pattern recognition

论文评审过程：Received 7 September 2019, Revised 28 February 2020, Accepted 6 March 2020, Available online 6 March 2020, Version of Record 13 March 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113364