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Open AccessArticle

Combined Conflict Evidence Based on Two-Tuple IOWA Operators

by Ying Zhou 1,*, Xiyun Qin 2 and Xiaozhe Zhao 2
1
School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China
2
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(11), 1369; https://doi.org/10.3390/sym11111369
Received: 15 October 2019 / Revised: 30 October 2019 / Accepted: 1 November 2019 / Published: 4 November 2019
Due to poor natural factors and human interference, the information that was obtained by sensors tends to have high uncertainty and high conflict with others. A combination of highly conflicting evidence with Dempster’s rule often produces results that run counter to intuition. To solve the above problem, a conflict evidence combination methodology is proposed in this article, which contains the distance of evidence, classical conflict coefficient, and two-tuple IOWA operator. Both the classical conflict coefficient and Jousselme distance indicate the degree of evidence conflict, and it is clear that the two parameters are symmetrical. First, the two-tuple IOWA operator is proposed. Second, the orness is determined by aggregated data; then, the weighting vector is calculated by a maximal entropy method. Finally, the weighted average is the evidence in the system by a two-tuple IOWA operator; then, the Dempster combination rule is utilized to fuse information. Compared with other existing methods, the presented methodology has high performance when dealing with conflict evidence and has strong anti-interference ability. View Full-Text
Keywords: two-tuple IOWA operator; weighting vector; Dempster’s rule; distance of evidence; conflict evidence two-tuple IOWA operator; weighting vector; Dempster’s rule; distance of evidence; conflict evidence
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Zhou, Y.; Qin, X.; Zhao, X. Combined Conflict Evidence Based on Two-Tuple IOWA Operators. Symmetry 2019, 11, 1369.

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