Maximum decision entropy-based attribute reduction in decision-theoretic rough set model

作者：

Highlights：

• The potential problems of the existing measures in the decision-theoretic rough set model are examined.

• A novel monotonic measure using maximum decision entropy is proposed.

• A heuristic attribute reduction algorithm with the objective of max-relevance and min-redundancy is provided.

摘要

•The potential problems of the existing measures in the decision-theoretic rough set model are examined.•A novel monotonic measure using maximum decision entropy is proposed.•A heuristic attribute reduction algorithm with the objective of max-relevance and min-redundancy is provided.

论文关键词：Decision-theoretic rough set model,Attribute reduction,Maximum decision entropy,Decision monotonicity

论文评审过程：Received 3 June 2017, Revised 9 December 2017, Accepted 11 December 2017, Available online 12 December 2017, Version of Record 3 February 2018.

论文官网地址：https://doi.org/10.1016/j.knosys.2017.12.014