Abstract
Dempster–Shafer (D–S) evidence theory is widely used in the field of uncertain information processing, but because of its defective fusion rules, it often results in visual violations when dealing with highly conflicting evidence. To solve the conflict management problem of D–S theory, an improved belief \(\chi 2\) divergence (called \({\mathcal {I}}{\mathcal {B}}\chi ^2\)) is proposed in this paper. The \({\mathcal {I}}{\mathcal {B}}\chi ^2\) divergence takes into account the amount of all possible hypotheses, which allow it be a more credible and efficient solution to measure the dissimilarity between evidences. Moreover, it has good mathematical properties including symmetry, boundedness and non-degeneracy. Next, we designed a novel multi-resource information fusion algorithm based on \({\mathcal {I}}{\mathcal {B}}\chi ^2\) divergence. Finally, application in automobile system fault recognition and iris feature recognition prove the effectiveness and accuracy of the proposed multi-resource information fusion algorithm.
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Notes
A Generalized \(\chi \)2 Divergence for multisource Information Fusion.
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Acknowledgements
The authors greatly appreciate the reviewers’ suggestions and the editor’s encouragement. This research is supported by the National Natural Science Foundation of China (No. 62003280), Chongqing Talents: Exceptional Young Talents Project (cstc2022ycjh-bgzxm0070), and Chongqing Overseas Scholars Innovation Program (No. cx2022024).
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Communicated by Graçaliz Pereira Dimuro.
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Gao, X., Xiao, F. An improved belief \(\chi ^2\) divergence for Dempster–Shafer theory and its applications in pattern recognition. Comp. Appl. Math. 41, 277 (2022). https://doi.org/10.1007/s40314-022-01975-3
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DOI: https://doi.org/10.1007/s40314-022-01975-3
Keywords
- Multi-resource information fusion
- Dempster–Shafer theory
- \(\chi ^2\) divergence
- Belief \(\chi ^2\) divergence
- Pattern recognition