Ensemble feature selection using distance-based supervised and unsupervised methods in binary classification
作者：
Highlights：
• Proposing formulas for distance-based supervised and unsupervised feature selection
• Applying an ensemble of the proposed methods to select the best subset of features
• Evaluating the effectiveness of 24 distance measures on the proposed methods
• Maximizing relevancy and minimizing redundancy of features in unsupervised process
• Investigating high-dimensional datasets proves the efficiency of the proposed methods
摘要
•Proposing formulas for distance-based supervised and unsupervised feature selection•Applying an ensemble of the proposed methods to select the best subset of features•Evaluating the effectiveness of 24 distance measures on the proposed methods•Maximizing relevancy and minimizing redundancy of features in unsupervised process•Investigating high-dimensional datasets proves the efficiency of the proposed methods
论文关键词：Feature selection,Supervised feature selection,Unsupervised feature selection,Distance measure,Filtering
论文评审过程：Received 23 May 2021, Revised 2 September 2021, Accepted 27 February 2022, Available online 18 March 2022, Version of Record 30 March 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.116794
