Abstract
Dempster–Shafer evidence theory is widely applied in various fields related to information fusion. However, the results are counterintuitive when highly conflicting evidence is fused with Dempster’s rule of combination. Many improved combination methods have been developed to address conflicting evidence. Nevertheless, all of these approaches have inherent flaws. To solve the existing counterintuitive problem more effectively and less conservatively, an improved combination method for conflicting evidence based on the redistribution of the basic probability assignment is proposed. First, the conflict intensity and the unreliability of the evidence are calculated based on the consistency degree, conflict degree and similarity coefficient among the evidence. Second, the redistribution equation of the basic probability assignment is constructed based on the unreliability and conflict intensity, which realizes the redistribution of the basic probability assignment. Third, to avoid excessive redistribution of the basic probability assignment, the precision degree of the evidence obtained by information entropy is used as the correction factor to modify the basic probability assignment for the second time. Finally, Dempster’s rule of combination is used to fuse the modified basic probability assignment. Several different types of examples and actual data sets are given to illustrate the effectiveness and potential of the proposed method. Furthermore, the comparative analysis reveals the proposed method to be better at obtaining the right results than other related methods.
Highlights
With the emergence of various big data platforms, it is becoming easier to access multiple sources of information describing the same research object
The proposed method is able to deal with cases that cannot be solved by other methods, which expands the application of D-S theory
To solve the paradoxical problem of multisource information fusion, an improved combination method for conflicting evidence based on the redistribution of the basic probability assignment (BPA) is proposed
Summary
With the emergence of various big data platforms, it is becoming easier to access multiple sources of information describing the same research object. The main challenge is how to fuse these information sources while eliminating interference and preserving the truth. As an important method for modelling and processing uncertain information fusion [1, 2], Dempster–Shafer (D-S) evidence theory is an effective tool to fuse information from. The paradox shows that counterintuitive results obtain when fusing highly conflicting evidence by using Dempster’s rule of combination. To address this issue, hundreds of methods have been developed [18,19,20,21,22], and they can be divided into three main categories: (1) modifying Dempster’s rule of combination; (2) preprocessing the bodies of evidence; and (3) modifying the closed-world assumption
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