Evaluation of fuzzy membership functions for linguistic rule-based classifier focused on explainability, interpretability and reliability

作者：

Highlights：

• Efficient algorithm of rule extraction provides interpretable data-driven knowledge.

• Fuzzy rules and belief measure ensure classification reliability and explainability.

• New rule evaluation method allows extracting effective rule-based knowledge.

• Approach achieves competitive generalization quality with the simplest knowledge base.

摘要

•Efficient algorithm of rule extraction provides interpretable data-driven knowledge.•Fuzzy rules and belief measure ensure classification reliability and explainability.•New rule evaluation method allows extracting effective rule-based knowledge.•Approach achieves competitive generalization quality with the simplest knowledge base.

论文关键词：Explainable decision support,Fuzzy belief function,Interpretable classification,Membership function evaluation

论文评审过程：Received 14 June 2021, Revised 16 December 2021, Accepted 28 March 2022, Available online 6 April 2022, Version of Record 12 April 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117116