A Note on Incomplete Information Modeling in the Evidence Theory

2020

IEEE Access

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Dempster-Shafer evidence theory plays an important role in many applications such as multi-sensor data fusion and pattern recognition. However, if there are conflicts among evidences, the results of data fusion using Dempster combination rule may lead to counter-intuitive results. In this paper, we propose a new method named elementary belief assignment function for conflict data fusion. The proposed method aims at getting a more rational data fusion result by preprocessing the mass function before implementing data fusion with Dempster's combination rule. The elementary belief assignment function takes into consideration not only the number of focal elements in the current body of evidence but also the proposition in the power set space. By assigning the mass value of potential conflict focal element to other related propositions in the power set space, we can reduce the conflict level among different bodies of evidences effectively. We verify the rationality and efficiency of the proposed method according to several experiment examples. INDEX TERMS Dempster-Shafer evidence theory, conflict management, conflict data fusion, basic belief assignment, elementary belief assignment function.

Section: Discussionmentioning

confidence: 99%

An Improved Method to Manage Conflict Data Using Elementary Belief Assignment Function in the Evidence Theory

2020

IEEE Access

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“…In addition to FMEA processes, uncertain information can be processed in practical applications such as distribution networks [ 20 ], Mechatronics Engineering [ 21 ], nuclear industry [ 22 ], infomation fusion and optimization [ 23 ], and so on [ 24 ]. To deal with the fuzziness in information processing, many theories and methods have been presented, such as Dempster–Shafer evidence theory (DST) [ 25 , 26 ], probability theory [ 27 , 28 ], information entropy [ 29 ], fuzzy sets theory [ 30 ], and so on [ 31 , 32 ]. For example, in [ 33 ], a new risk measure combined with information entropy to measure risk in clustering is proposed.…”

Section: Introductionmentioning

confidence: 99%

Deng Entropy Weighted Risk Priority Number Model for Failure Mode and Effects Analysis

Zheng

2020

Entropy

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Failure mode and effects analysis (FMEA), as a commonly used risk management method, has been extensively applied to the engineering domain. A vital parameter in FMEA is the risk priority number (RPN), which is the product of occurrence (O), severity (S), and detection (D) of a failure mode. To deal with the uncertainty in the assessments given by domain experts, a novel Deng entropy weighted risk priority number (DEWRPN) for FMEA is proposed in the framework of Dempster–Shafer evidence theory (DST). DEWRPN takes into consideration the relative importance in both risk factors and FMEA experts. The uncertain degree of objective assessments coming from experts are measured by the Deng entropy. An expert’s weight is comprised of the three risk factors’ weights obtained independently from expert’s assessments. In DEWRPN, the strategy of assigning weight for each expert is flexible and compatible to the real decision-making situation. The entropy-based relative weight symbolizes the relative importance. In detail, the higher the uncertain degree of a risk factor from an expert is, the lower the weight of the corresponding risk factor will be and vice versa. We utilize Deng entropy to construct the exponential weight of each risk factor as well as an expert’s relative importance on an FMEA item in a state-of-the-art way. A case study is adopted to verify the practicability and effectiveness of the proposed model.

“…Third, during the process of calculating entropy, some characteristics of the basic probability assignment are lost. Fourth, the evidence in the open world assumption needs further consideration [ 26 , 27 ]. The uncertainty measure including belief entropy can be a promising method for uncertain information modeling, processing, and management of conflict in DS theory [ 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved Total Uncertainty Measure in the Evidence Theory and Its Application in Decision Making

Qin

Wen

2020

Entropy

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Dempster–Shafer evidence theory (DS theory) has some superiorities in uncertain information processing for a large variety of applications. However, the problem of how to quantify the uncertainty of basic probability assignment (BPA) in DS theory framework remain unresolved. The goal of this paper is to define a new belief entropy for measuring uncertainty of BPA with desirable properties. The new entropy can be helpful for uncertainty management in practical applications such as decision making. The proposed uncertainty measure has two components. The first component is an improved version of Dubois–Prade entropy, which aims to capture the non-specificity portion of uncertainty with a consideration of the element number in frame of discernment (FOD). The second component is adopted from Nguyen entropy, which captures conflict in BPA. We prove that the proposed entropy satisfies some desired properties proposed in the literature. In addition, the proposed entropy can be reduced to Shannon entropy if the BPA is a probability distribution. Numerical examples are presented to show the efficiency and superiority of the proposed measure as well as an application in decision making.