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Earth Mover’s divergence of belief function

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Abstract

Divergence is used to measure the difference of two systems, and it is widely applied in many fields. To solve this problem more efficiently, Dempster–Shafer evidence theory has been proposed, different from the traditional probability distribution, and because of its processing advantages of uncertainty, has been widely used in many aspects of reality. In this paper, a new method of belief divergence measure of mass functions is proposed, named as Earth Mover’s divergence of belief function, which is a generalization of Earth Mover’s distance (Wasserstein distance). Compared with other existing methods of divergence measuring, the EM divergence can show good performance in the presence of higher degrees of uncertainty and more conflicts. Numerical examples help have a better understanding of the Earth Mover’s divergence of belief function. Based on the new method of belief divergence measure, there is a combination model proposed to address the problem of data fusion. Application in target recognition is used to show the efficiency of the proposed method of divergence measure.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (No. 62003280), Chongqing Talents: Exceptional Young Talents Project (No. cstc2022ycjh-bgzxm0070), and Chongqing Overseas Scholars Innovation Program (No. cx2022024). The authors greatly appreciate the reviewers’ suggestions and editor’s encouragement.

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Authors and Affiliations

  1. School of Computer Science and Software, Southwest University of China, Chongqing, 400715, China

    Peilin Liu

  2. School of Big Data and Software Engineering, Chongqing University, Chongqing, 401331, China

    Peilin Liu & Fuyuan Xiao

  3. National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Xi’an, China

    Fuyuan Xiao

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  1. Peilin Liu
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Correspondence to Fuyuan Xiao.

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Communicated by Graçaliz Pereira Dimuro.

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Liu, P., Xiao, F. Earth Mover’s divergence of belief function. Comp. Appl. Math. 41, 292 (2022). https://doi.org/10.1007/s40314-022-02000-3

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  • DOI: https://doi.org/10.1007/s40314-022-02000-3

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