Fault recognition using an ensemble classifier based on Dempster–Shafer Theory
作者：
Highlights：
• We propose an effective combination method based on Dempster–Shafer Theory for ensemble classifiers.
• The proposed method measures the outputs of member classifiers using a combinational weight.
• The combinational weight consists of objective weight and subjective weight.
• The objective weight is defined based on the support degree among classifiers.
• The subjective weight is defined based on support degree of within classifier.
摘要
•We propose an effective combination method based on Dempster–Shafer Theory for ensemble classifiers.•The proposed method measures the outputs of member classifiers using a combinational weight.•The combinational weight consists of objective weight and subjective weight.•The objective weight is defined based on the support degree among classifiers.•The subjective weight is defined based on support degree of within classifier.
论文关键词：Fault recognition,Ensemble classifier,Dempster–Shafer Theory,Correlation entropy,Evidence weight
论文评审过程：Received 21 January 2019, Revised 8 August 2019, Accepted 12 October 2019, Available online 14 October 2019, Version of Record 21 October 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.107079
