Applying Dempster–Shafer theory for developing a flexible, accurate and interpretable classifier
作者：
Highlights：
• Classification method that combines machine learning techniques and expert systems.
• Tables indicates the contribution for each attribute, allowing interpretability.
• Gradient descent is used to optimize the values for the weights for each rule.
• Results are comparable to other classification methods but being interpretable.
• The proposed method is general and can be easily applied to other scenarios.
摘要
•Classification method that combines machine learning techniques and expert systems.•Tables indicates the contribution for each attribute, allowing interpretability.•Gradient descent is used to optimize the values for the weights for each rule.•Results are comparable to other classification methods but being interpretable.•The proposed method is general and can be easily applied to other scenarios.
论文关键词：Supervised learning,Expert systems,Gradient descent,Dempster-Shafer theory,Interpretability
论文评审过程：Received 15 July 2019, Revised 2 December 2019, Accepted 29 January 2020, Available online 30 January 2020, Version of Record 6 February 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113262
