General assembly framework for online streaming feature selection via Rough Set models

作者：

Highlights：

• We summarize online streaming feature selection into three main components.

• Formal definitions of feature relationships from the Rough Set perspective.

• A novel general framework can assemble new algorithms for different problems.

• Experiments on four new derived algorithms indicate the efficiency.

摘要

•We summarize online streaming feature selection into three main components.•Formal definitions of feature relationships from the Rough Set perspective.•A novel general framework can assemble new algorithms for different problems.•Experiments on four new derived algorithms indicate the efficiency.

论文关键词：Feature selection,Online feature selection,Streaming features,General assembly framework,Rough Set models

论文评审过程：Received 21 August 2021, Revised 11 April 2022, Accepted 4 May 2022, Available online 16 May 2022, Version of Record 21 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117520