A systematic evaluation of filter Unsupervised Feature Selection methods

作者：

Highlights：

• A systematic evaluation of filter Unsupervised Feature Selection methods is presented.

• The most popular and recent filter UFS methods are included in our study.

• The evaluation of the filter UFS methods followed the standards in the literature.

• A general discussion based on the results of the evaluated methods is provided.

• Some guidelines for the use of the evaluated filter UFS methods is also provided.

摘要

•A systematic evaluation of filter Unsupervised Feature Selection methods is presented.•The most popular and recent filter UFS methods are included in our study.•The evaluation of the filter UFS methods followed the standards in the literature.•A general discussion based on the results of the evaluated methods is provided.•Some guidelines for the use of the evaluated filter UFS methods is also provided.

论文关键词：Dimensionality reduction,Unsupervised Feature Selection,Filter approach,High dimensional data

论文评审过程：Received 3 April 2019, Revised 9 May 2020, Accepted 11 July 2020, Available online 25 July 2020, Version of Record 5 August 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113745