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An Improved Belief Entropy and Its Application in Decision-Making

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  • Deyun Zhou
  • Yongchuan Tang
  • Wen Jiang

Abstract

Uncertainty measure in data fusion applications is a hot topic; quite a few methods have been proposed to measure the degree of uncertainty in Dempster-Shafer framework. However, the existing methods pay little attention to the scale of the frame of discernment (FOD), which means a loss of information. Due to this reason, the existing methods cannot measure the difference of uncertain degree among different FODs. In this paper, an improved belief entropy is proposed in Dempster-Shafer framework. The proposed belief entropy takes into consideration more available information in the body of evidence (BOE), including the uncertain information modeled by the mass function, the cardinality of the proposition, and the scale of the FOD. The improved belief entropy is a new method for uncertainty measure in Dempster-Shafer framework. Based on the new belief entropy, a decision-making approach is designed. The validity of the new belief entropy is verified according to some numerical examples and the proposed decision-making approach.

Suggested Citation

  • Deyun Zhou & Yongchuan Tang & Wen Jiang, 2017. "An Improved Belief Entropy and Its Application in Decision-Making," Complexity, Hindawi, vol. 2017, pages 1-15, March.
    • Handle: RePEc:hin:complx:4359195
    DOI: 10.1155/2017/4359195
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    References listed on IDEAS

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    Cited by:

    1. Dingyi Gan & Bin Yang & Yongchuan Tang, 2020. "An Extended Base Belief Function in Dempster–Shafer Evidence Theory and Its Application in Conflict Data Fusion," Mathematics, MDPI, vol. 8(12), pages 1-19, December.

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