A feature cluster taxonomy based feature selection technique

作者：

Highlights：

• FCTFS works in both autonomous and user guided mode.

• The defined taxonomy helps in arriving at optimal number of good quality clusters.

• Feature elimination due to irrelevance and redundancy is clearly isolated.

• It is faster than traditional search based methods.

• Yields superior results compared to some state of the art methods over 24 data sets.

摘要

•FCTFS works in both autonomous and user guided mode.•The defined taxonomy helps in arriving at optimal number of good quality clusters.•Feature elimination due to irrelevance and redundancy is clearly isolated.•It is faster than traditional search based methods.•Yields superior results compared to some state of the art methods over 24 data sets.

论文关键词：Feature selection,Feature cluster,Redundancy,Entropy,Coefficient of variation

论文评审过程：Received 12 June 2016, Revised 6 October 2016, Accepted 25 January 2017, Available online 3 February 2017, Version of Record 3 March 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.01.044