Feature Selection for Classification using Principal Component Analysis and Information Gain
作者：
Highlights：
• Feature selection improves performance of machine learning algorithms.
• Feature selection with more n-tier techniques is simpler and more stable.
• A feature selection model that is not specific to any data set is widely applied.
摘要
•Feature selection improves performance of machine learning algorithms.•Feature selection with more n-tier techniques is simpler and more stable.•A feature selection model that is not specific to any data set is widely applied.
论文关键词：Feature selection,Classification,Dimensionality reduction,Filter model,Information gain,Principal component analysis
论文评审过程：Received 6 November 2020, Revised 8 January 2021, Accepted 18 February 2021, Available online 26 February 2021, Version of Record 8 March 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.114765
