A Fuzzy Preference-Based Dempster-Shafer Evidence Theory for Decision Fusion

HARI MOHAN PANDEY, Chaosheng Zhu, Bowen Qin, Fuyuan Xiao, Zehong Cao

Research output: Contribution to journalArticle (journal)peer-review

43 Citations (Scopus)
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Dempster-Shafer evidence theory (D-S) is an effective instrument for merging the collected pieces of basic probability assignment (BPA), and it exhibits superiority in achieving robustness of soft computing and decision making in an uncertain and imprecise environment. However, the determination of BPA is still uncertain, and merely applying evidence theory can sometimes lead to counterintuitive results when lines of evidence conflict. In this paper, a novel BPA generation method for binary problems called as the base algorithm is designed based on the kernel density estimation to construct the probability density function models, using the pairwise learning method to establish binary classification pairs. By means of the new BPA generation method, a new decision-making algorithm based on D-S evidence theory, fuzzy preference relation and nondominance criterion is effectively designed. The strength of the proposed method is presented in applying pairwise learning, which
transforms the original complex problem into simple subproblems. With this process, the complexity of the problem to be solved is greatly reduced, which increases the feasibility for industrial applications. Furthermore, the fuzzy computing technique is used to aggregate the output for each single subproblem, and the nondominance degree of each class is determined from the fuzzy preference relation matrix, which can be directly used for the determination of the input instance. Based on several industrial-based classification experiments, the proposed BPA generation method
and decision-making algorithm present the effectiveness and improvement in terms of precision and Cohen’s kappa.
Original languageEnglish
Article numberINS-D-20-3940R2
Pages (from-to)306-3022
Number of pages17
JournalInformation Sciences
Early online date16 Apr 2021
Publication statusE-pub ahead of print - 16 Apr 2021


  • Multisource data fusion
  • Pairwise learning
  • Dempster–Shafer evidence theory
  • Fuzzy preference relationship
  • Basic probability assignment generation
  • Kernel density estimation
  • Decision making
  • Classification


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