A New Reliability Coefficient Using Betting Commitment Evidence Distance in Dempster–Shafer Evidence Theory for Uncertain Information Fusion

Tang, Yongchuan; Wu, Shuaihong; Zhou, Ying; Huang, Yubo; Zhou, Deyun

doi:10.3390/e25030462

Cited by 8 publications

(2 citation statements)

References 73 publications

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“…The D–S evidence theory, also known as Dempster's theory of evidence or simply Dempster's rule, is a mathematical framework for reasoning under uncertainty and combining evidence from multiple sources [33]. Developed by Arthur P. Dempster in the 1960s, this theory has found applications in various fields such as artificial intelligence, decision‐making, and information fusion.…”

Section: Literature Reviewmentioning

confidence: 99%

From data inference to evidence fusion: A methodology for studying computer ethics at different levels

Piao,

Lin,

Song

et al. 2024

Comp Applic In Engineering

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The rapid development and widespread use of computer technology in today's society have brought significant benefits and convenience to individuals and organizations. The study presents a concept for constructing a research framework in the realm of computer ethics impact. This involves the introduction of an information fusion model and the utilization of a Dempster–Shafer (D–S) evidence theory algorithm. The information fusion model aims to integrate multiple sources of evidence related to computer ethics, including expert opinions, public attitudes, and legal regulations. Furthermore, the experimental results show that the platform's impact perception was effective in reducing the number of individuals who consider computer ethics unnecessary from 40% to 3%. This finding highlights the potential of this platform for enhancing public awareness and understanding of computer ethics. In conclusion, the proposed information fusion model and D–S evidence theory algorithm offer a valuable tool for conducting early research on the construction of computer ethics. It is of utmost importance to delve into the uncontrollable facets of building computer ethics and to navigate the subject–object dilemma from the standpoint of computer users. This approach is essential in addressing the ethical dilemmas that have arisen due to the rapid advancement of computer technology in today's world. The novelty lies in the research methodology used in this study, substantial public perception change, ethical risk reduction, and subject–object issue in computer ethics resolution all substantially contribute to the field's influence on people, organizations, and societies.

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Section: Literature Reviewmentioning

confidence: 99%

From data inference to evidence fusion: A methodology for studying computer ethics at different levels

Piao,

Lin,

Song

et al. 2024

Comp Applic In Engineering

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“…In our recent work [86], we combined the Jousselme distance and the cosine value to determine the conflict degree between the bodies of evidence, and we adopted Deng entropy to measure the uncertainty degree of each body of evidence; we used both conflict and uncertainty measurements to construct weighting factors to modify the original mass functions. Tang et al [88] proposed a new approach to pre-process evidence by introducing a reliability coefficient based on the betting commitment evidence distance and the single-factor belief function. The betting commitment is constructed on the basis of the pignistic probability function to measure the dissimilarity between two BPAs, while the single factor belief function is a single subset used to evenly distribute the probability to each subset of the power set.…”

Section: Introductionmentioning

confidence: 99%

Multisensor Data Fusion in IoT Environments in Dempster–Shafer Theory Setting: An Improved Evidence Distance-Based Approach

Hamda

Hadjali

Lagha

2023

Sensors

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In IoT environments, voluminous amounts of data are produced every single second. Due to multiple factors, these data are prone to various imperfections, they could be uncertain, conflicting, or even incorrect leading to wrong decisions. Multisensor data fusion has proved to be powerful for managing data coming from heterogeneous sources and moving towards effective decision-making. Dempster–Shafer (D–S) theory is a robust and flexible mathematical tool for modeling and merging uncertain, imprecise, and incomplete data, and is widely used in multisensor data fusion applications such as decision-making, fault diagnosis, pattern recognition, etc. However, the combination of contradictory data has always been challenging in D–S theory, unreasonable results may arise when dealing with highly conflicting sources. In this paper, an improved evidence combination approach is proposed to represent and manage both conflict and uncertainty in IoT environments in order to improve decision-making accuracy. It mainly relies on an improved evidence distance based on Hellinger distance and Deng entropy. To demonstrate the effectiveness of the proposed method, a benchmark example for target recognition and two real application cases in fault diagnosis and IoT decision-making have been provided. Fusion results were compared with several similar methods, and simulation analyses have shown the superiority of the proposed method in terms of conflict management, convergence speed, fusion results reliability, and decision accuracy. In fact, our approach achieved remarkable accuracy rates of 99.32% in target recognition example, 96.14% in fault diagnosis problem, and 99.54% in IoT decision-making application.

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Rolling Bearing Fault Diagnosis Based on Multi-source Information Fusion

Zhu,

Deng,

Xing

et al. 2024

J Fail. Anal. and Preven.

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A New Reliability Coefficient Using Betting Commitment Evidence Distance in Dempster–Shafer Evidence Theory for Uncertain Information Fusion

Cited by 8 publications

References 73 publications

From data inference to evidence fusion: A methodology for studying computer ethics at different levels

From data inference to evidence fusion: A methodology for studying computer ethics at different levels

Multisensor Data Fusion in IoT Environments in Dempster–Shafer Theory Setting: An Improved Evidence Distance-Based Approach

Rolling Bearing Fault Diagnosis Based on Multi-source Information Fusion

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