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A Novel Complex Pignistic Belief Transform for Conflict Measure in Complex Evidence Theory

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Data Mining and Big Data (DMBD 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1453))

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Abstract

Information fusion has received extensive attention in the past decades, in which the handing of uncertain information is still an open issue. Complex evidence theory (CET) can effectively deal with uncertain information. However, in CET, the measurement of conflict is still a problem to be solved. Therefore, this paper proposes a novel method of measurement method for CET. Firstly, we divide the belief into power sets and propose a complex pignistic belief transform. Then the betting commitment function is designed. Finally, a new betting distance based on betting commitment function is proposed, and some properties of the betting distance are analyzed, including the non-negativeness, nondegeneracy, symmetry, and triangle inequality. The betting distance can be used to measure the conflict between CBBAs. In addition, an example is given to show the advantage of betting distance compared to the conflict coefficient.

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

    Yangyang Zhao

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

    Yangyang Zhao & Fuyuan Xiao

Authors
  1. Yangyang Zhao
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  2. Fuyuan Xiao
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Corresponding author

Correspondence to Fuyuan Xiao .

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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Zhao, Y., Xiao, F. (2021). A Novel Complex Pignistic Belief Transform for Conflict Measure in Complex Evidence Theory. 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 1453. Springer, Singapore. https://doi.org/10.1007/978-981-16-7476-1_17

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  • DOI: https://doi.org/10.1007/978-981-16-7476-1_17

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7475-4

  • Online ISBN: 978-981-16-7476-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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