4.7 Article

Generalized combination rule for evidential reasoning approach and Dempster-Shafer theory of evidence

期刊

INFORMATION SCIENCES
卷 547, 期 -, 页码 1201-1232

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2020.07.072

关键词

Decision analysis; Generalized combination rule; Evidential reasoning; Dempster-Shafer theory of evidence; Weight and reliability

资金

  1. Major Program of National Social Science Foundation of China [18ZDA055]
  2. National Natural Science Foundation of China (NSFC) [71874167, 71804170, 71901199, 71462022]
  3. Fundamental Research Funds for the Central Universities [202041005]
  4. Special Funds of Taishan Scholars Project of Shandong Province [tsqn20171205]

向作者/读者索取更多资源

This study establishes a generalized combination (GC) rule with both weight and reliability to address the infeasibilities in the evidential reasoning (ER) approach, showing superiority through numerical comparisons and discussions.
The Dempster-Shafer (DS) theory of evidence can combine evidence with one parameter. The evidential reasoning (ER) approach is an extension of DS theory that can combine evidence with two parameters (weights and reliabilities). However, it has three infeasible aspects: reliability dependence, unreliability effectiveness, and intergeneration inconsistency. This study aimed to establish a generalized combination (GC) rule with both weight and reliability, where ER and DS can be viewed as two particular cases, and the problems of infeasibility of the parameters can be solved. In this paper, the infeasibilities of ER are analyzed, and a generalized discounting method is introduced to reasonably discount the belief distributions of the evidence using both the weight and the reliability. A GC rule is then constructed to combine evidence by means of the orthogonal sum operation, and the corresponding theorems and corollaries are provided. Finally, the superiority of the GC rule is shown through numerical comparisons and discussion, and an illustrative example is provided to demonstrate its applicability. (C) 2020 Elsevier Inc. All rights reserved.

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