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A Generalized \(\chi ^2\) Divergence for Multisource Information Fusion

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Data Mining and Big Data (DMBD 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1454))

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Abstract

Divergence measure has been extensively applied in many fields. Basic probability assignment (BPA), instead of probability, is adopted to represent the belief degree of elements in Dempster-Shafer theory. But how to measure the divergence among BPAs is still under research. This paper proposes a novel belief divergence measure based on classic \(\chi ^2\) divergence. Comparing to the existing divergence, the new proposed divergence performs better in measuring discrepancy among BPAs. In addition, the proposed divergence is proved be a bounded, non-degenerated and symmetrical divergence measure. Numerical examples are presented to describe the effectiveness of proposed divergence measure.

Supported by the National Natural Science Foundation of China (No. 62003280).

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Author information

Authors and Affiliations

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

    Xueyuan Gao

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

    Xueyuan Gao & Fuyuan Xiao

Authors
  1. Xueyuan Gao
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  2. Fuyuan Xiao
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Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. The University of Sydney, Sydney, Australia

    Albert Zomaya

  4. Guangzhou University, Guangzhou, China

    Hongyang Yan

  5. Guangdong Polytechnic Normal University, Guangzhou, China

    Jun Cai

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Gao, X., Xiao, F. (2021). A Generalized \(\chi ^2\) Divergence for Multisource Information Fusion. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1454. Springer, Singapore. https://doi.org/10.1007/978-981-16-7502-7_20

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  • DOI: https://doi.org/10.1007/978-981-16-7502-7_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7501-0

  • Online ISBN: 978-981-16-7502-7

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