A novel approach to attribute reduction based on weighted neighborhood rough sets

作者：

Highlights：

• The existing researches on neighborhood rough sets use the same attribute weights.

• The attributes that are highly related to decisions should be highlighted.

• We introduce partition coefficients of attributes to re-assign weights of attributes.

• The results show WNRS can get higher classification accuracy and compression ratio.

摘要

•The existing researches on neighborhood rough sets use the same attribute weights.•The attributes that are highly related to decisions should be highlighted.•We introduce partition coefficients of attributes to re-assign weights of attributes.•The results show WNRS can get higher classification accuracy and compression ratio.

论文关键词：Weighted neighborhood rough sets,Attribute reduction,Neighborhood rough sets,Information tables

论文评审过程：Received 14 October 2020, Revised 23 February 2021, Accepted 25 February 2021, Available online 27 February 2021, Version of Record 9 March 2021.

论文官网地址：https://doi.org/10.1016/j.knosys.2021.106908