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A conflict evidence fusion method based on the composite discount factor and the game theory.

Authors :
Liu, Xiaoyang
Liu, Shulin
Xiang, Jiawei
Sun, Ruixue
Source :
Information Fusion. Jun2023, Vol. 94, p1-16. 16p.
Publication Year :
2023

Abstract

• A binary function is devised to measure the conflict between any pieces of evidence. • A new method is proposed for conflict evidence fusion. • The method is based on composite discount factor and game theory. • The method achieves the optimal coordination between multiple conflict measurements. • The method is superior to the relevant methods with better effectiveness. Dempster–Shafer (D–S) evidence theory is widely used in various fields of information fusion. However, it is still an open issue that the D–S evidence theory may produce the counter–intuitive results in fusing high–conflict evidences. Aim at this problem, a novel conflict evidence fusion method based on the composite discount factor and the game theory is proposed in this paper. Firstly, an improved Shafer's conflict measurement formula based on the Jaccard similarity coefficient is devised, and combined with the Jousselme distance into a novel binary function to measure the global conflict between evidences as the evidence falsity. Then, the local conflict between evidences and the information volume of evidences are measured by using the Jousselme distance and belief entropy to indicate the credibility and uncertainty of evidences. Next, based on the game theory, the falsity, credibility and uncertainty are weighted and combined into the composite discount factors to correct each body of evidence (BOE). Ultimately, all corrected evidences are fused by Dempster's combination rule to obtain the final result. Two numerical examples are given to verify that the proposed method is effective and feasible, which outperforms the previous methods in handling the conflict evidences. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15662535
Volume :
94
Database :
Academic Search Index
Journal :
Information Fusion
Publication Type :
Academic Journal
Accession number :
162028574
Full Text :
https://doi.org/10.1016/j.inffus.2023.01.009