Accelerating information entropy-based feature selection using rough set theory with classified nested equivalence classes

作者：

Highlights：

• Proposed a CNEC-based approach for information-entropy-based significance.

• Extracts knowledge from a decision table to reduce the universe and construct CNECs.

• Decreased the number of inner significance calculations using one type of CNEC.

• Presented a general heuristic core computation and feature selection algorithm.

摘要

•Proposed a CNEC-based approach for information-entropy-based significance.•Extracts knowledge from a decision table to reduce the universe and construct CNECs.•Decreased the number of inner significance calculations using one type of CNEC.•Presented a general heuristic core computation and feature selection algorithm.

论文关键词：Feature selection,Rough set theory,Attribute reduction,Information entropy

论文评审过程：Received 11 May 2019, Revised 12 May 2020, Accepted 23 June 2020, Available online 25 June 2020, Version of Record 6 July 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107517