Basic probability assignment to probability distribution function based on the Shapley value approach

Abstract

In Dempster–Shafer evidence theory, how to use the basic probability assignment (BPA) in decision‐making is a significant issue. The transformation of BPA into a probability distribution function is one of the common and feasible schemes. To overcome the problems of the existing methods, we propose a marginal probability transformation method based on the Shapley value approach. The proposed method allocates BPA values in terms of how much an element contributes to a set, which is an equitable and effective distribution mechanism. Furthermore, we use probabilistic information content to evaluate the effect of each transformation method. Moreover, some numerical examples are used to demonstrate the efficiency and feasibility of the proposed method. Further, two applications, target recognition, fault diagnosis are used to verify the superiority and effectiveness of the proposed method in practice.

Consensus algorithm based on verifiable quantum random numbers

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“…The generation of random numbers is a long‐standing problem. The randomness and unpredictability of random numbers largely affect the security level of cryptosystems 19,20 . In most cases, random numbers are the key factors that accompany great benefits, fairness, and security, and therefore have been an important research direction in the field of cryptography and information security.…”

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“…31 In addition, with the law of associative and commutative, Dempster's rule of combination flexibly provides uncertainty reasoning to better support decision-making. Hence, D-S evidence theory is widely applied in the fields of target recognition, 32 output control, 33 pattern classification, 34 decisionmaking, 35 and so on. 36,37 Nevertheless, how to manage the highly conflicting evidence remains an open issue, since counterintuitive outcomes may be generated in such kind of situation.…”

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

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