Probability transformation of mass function: A weighted network method based on the ordered visibility graph

Chen, Luyuan; Deng, Yong; Cheong, Kang Hao

doi:10.1016/j.engappai.2021.104438

Cited by 91 publications

(15 citation statements)

References 49 publications

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“…As BPAs are able to model uncertainty, mass function has been thoroughly studied and derived, such as information volume of mass function, 52 complex evidential quantum dynamical model, 53 and so on. 54 Definition 3 (Dempster's rule of combination). Let m 1 and m 2 be two BPAs.…”

Section: D-s Evidence Theorymentioning

confidence: 99%

A novel belief χ2 ${\chi }^{2}$ divergence for multisource information fusion and its application in pattern classification

Zhang

Xiao

2022

Int J of Intelligent Sys

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Dempster-Shafer (D-S) evidence theory is invaluable in the domain of multisource information fusion for handing uncertainty problems. However, there may be counter-intuitive phenomenon when facing highly conflicting information. In this paper, a novel symmetric enhanced belief χ 2 divergence measure, called SEBχ 2 , is proposed to measure the discrepancy between basic probability assignments (BPAs). The SEBχ 2 divergence consider the features of BPAs as the influence of both single-element subsets and multielement subsets is taken into account. Furthermore, the SEBχ 2 divergence is proven to be symmetric, nonnegative and nondegenerate, which are desirable properties for conflict management. Then, a new algorithm for multisource information fusion based on the SEBχ 2 divergence measure is derived. Finally, an application for pattern classification is used to illustrate the superiority of the proposed SEBχ 2 divergence measure-based fusion method over other existing well-known and recent related works with a better classification accuracy of 94.39%.

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Section: D-s Evidence Theorymentioning

confidence: 99%

A novel belief χ2 ${\chi }^{2}$ divergence for multisource information fusion and its application in pattern classification

Zhang

Xiao

2022

Int J of Intelligent Sys

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“…As a classical data fusion theory, D-S evidence theory is widely applied in the field of uncertainty [54,55]. Here are some essential concepts:…”

Section: Basic Concepts In Belief Structurementioning

confidence: 99%

Uncertainty of Interval Type-2 Fuzzy Sets Based on Fuzzy Belief Entropy

Liu

Cai

2021

Entropy

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Interval type-2 fuzzy sets (IT2 FS) play an important part in dealing with uncertain applications. However, how to measure the uncertainty of IT2 FS is still an open issue. The specific objective of this study is to present a new entropy named fuzzy belief entropy to solve the problem based on the relation among IT2 FS, belief structure, and Z-valuations. The interval of membership function can be transformed to interval BPA [Bel,Pl]. Then, Bel and Pl are put into the proposed entropy to calculate the uncertainty from the three aspects of fuzziness, discord, and nonspecificity, respectively, which makes the result more reasonable. Compared with other methods, fuzzy belief entropy is more reasonable because it can measure the uncertainty caused by multielement fuzzy subsets. Furthermore, when the membership function belongs to type-1 fuzzy sets, fuzzy belief entropy degenerates to Shannon entropy. Compared with other methods, several numerical examples are demonstrated that the proposed entropy is feasible and persuasive.

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“…According to the measurement of uncertain information, many methods are used to identify the target and achieve the desired effect, such as evidential reasoning, 24 information fusion, 25 multidata fusion, 26,27 and so on. [28][29][30][31] Moreover, these methods have been widely used in various tasks, such as group decision-making (multiple attribute decision making-MADM), [32][33][34] reliability optimization, 35 medical system, 36,37 decision making, [38][39][40] fault diagnosis, [41][42][43] engineering system, 44 risk analysis, 45,46 and so on. [47][48][49][50] Regarding the processing of uncertain information, the most representative one is the D-S evidence theory.…”

Section: Introductionmentioning

confidence: 99%

Complex belief interval‐based distance measure with its application in pattern recognition

Zhang

Xiao

2022

Int J of Intelligent Sys

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The complex evidence theory is an effective methodology for multiattribute decision-making. Since difference measure between multiattribute plays an important role for conflict management in the process of multiattribute decision-making, how to measure discrepancy between complex basic belief assignments (CBBAs) in complex evidence theory is still an open issue. In this context, a new distance measurement (complex belief distance-CBD) is proposed in this paper by taking advantages of complex belief function and complex plausibility function, called complex belief interval-based distance. In addition, we compare the proposed CBD with the related work to illustrate its superiority. Next, based on CBD, we devise a novel multiattribute decision-making algorithm for pattern recognition. Finally, we apply the method to problems of medical diagnosis to verify the effectiveness of the proposed method.

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Probability transformation of mass function: A weighted network method based on the ordered visibility graph

Cited by 91 publications

References 49 publications

A novel belief χ2 ${\chi }^{2}$ divergence for multisource information fusion and its application in pattern classification

A novel belief χ2 ${\chi }^{2}$ divergence for multisource information fusion and its application in pattern classification

Uncertainty of Interval Type-2 Fuzzy Sets Based on Fuzzy Belief Entropy

Complex belief interval‐based distance measure with its application in pattern recognition

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