A fuzzy preference-based Dempster-Shafer evidence theory for decision fusion

Zhu, Chaosheng; Qin, Bowen; Xiao, Fuyuan; Cao, Zehong; Pandey, Hari Mohan

doi:10.1016/j.ins.2021.04.059

Cited by 47 publications

(10 citation statements)

References 51 publications

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“…Degree of evidence variation. In practice, there are conflicts among evidence [49]. Conflict is a kind of information related to the similarity of data features and is likely to have some value.…”

Section: Improved Algorithm Under Euclidean Distance Weighting Strategymentioning

confidence: 99%

Weighted assignment fusion algorithm of evidence conflict based on Euclidean distance and weighting strategy, and application in the wind turbine system

Gou

Zhang

et al. 2022

PLoS ONE

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In the process of intelligent system operation fault diagnosis and decision making, the multi-source, heterogeneous, complex, and fuzzy characteristics of information make the conflict, uncertainty, and validity problems appear in the process of information fusion, which has not been solved. In this study, we analyze the credibility and variation of conflict among evidence from the perspective of conflict credibility weight and propose an improved model of multi-source information fusion based on Dempster-Shafer theory (DST). From the perspectives of the weighting strategy and Euclidean distance strategy, we process the basic probability assignment (BPA) of evidence and assign the credible weight of conflict between evidence to achieve the extraction of credible conflicts and the adoption of credible conflicts in the process of evidence fusion. The improved algorithm weakens the problem of uncertainty and ambiguity caused by conflicts in the information fusion process, and reduces the impact of information complexity on analysis results. And it carries a practical application out with the fault diagnosis of wind turbine system to analyze the operation status of wind turbines in a wind farm to verify the effectiveness of the proposed algorithm. The result shows that under the conditions of improved distance metric evidence discrepancy and credible conflict quantification, the algorithm better shows the conflict and correlation among the evidence. It improves the accuracy of system operation reliability analysis, improves the utilization rate of wind energy resources, and has practical implication value.

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Section: Improved Algorithm Under Euclidean Distance Weighting Strategymentioning

confidence: 99%

Weighted assignment fusion algorithm of evidence conflict based on Euclidean distance and weighting strategy, and application in the wind turbine system

Gou

Zhang

et al. 2022

PLoS ONE

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“…Evidence theory (Denoeux, Introduction to belief functions, 2017) (Denoeux, Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence, 2008)-also known as DS or belief function theoryhas recently captured the attention of researchers as an essential tool for managing and dealing with uncertainty, imprecision, or a lack of information. This section will review the main concepts of evidence theory used throughout the article (Zhu, Qin, Xiao, Cao, & Pandey, 2021).…”

Section: Belief Function Theorymentioning

confidence: 99%

Data Fusion Technique for E-Learning Evaluation Based on Evidence Theory

Nassreddine,

Arid

2022

JCCE

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In recent years, all academic organizations turned to online learning to decrease the risk of virus infection during the coronavirus pandemic. The development of online classes produces new challenges for academic specialists, particularly in developing countries where technology devices are hardly available to participants. Even though some characteristics are shared between online and traditional on-campus learning, online learning needs more variables to assess, like student evaluation. Because of the different environments that participants are exposed to during online education, it is essential to discuss the concept of electronic learning environments. It calls for new techniques and methods to assess participants' knowledge and skills progress. This paper proposes a new approach to evaluating participants based on evidential theory and the data fusion concept. The evidential theory steadily spreads out because it is flexible in modeling the wide range of uncertainty inherent to natural environments. The proposed approach is adequate in dealing with all types of online courses. In addition, it permits educators to improve academic achievements by delivering regular participant feedback. The educator can redirect learning processes after exposing the feebleness of participants. The experimental results of three online university courses show this approach's efficacy.

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“…Motor imagery (MI) is the mental imagination of body movement without actual muscle movement and its corresponding rhythmic activities of the brain could be observed and applied as the input signals of BCI systems [4]. The regular rhythmic power changes in the sensorimotor area within mu (8)(9)(10)(11)(12) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) frequency bands are called event-related desynchronization/ synchronization (ERD/ERS), which can be detected to discriminate different kinds of MI tasks [5]. MI-based BCIs are flexible in their applications for consideration of MI not demanding external stimuli [6].…”

Section: Introductionmentioning

confidence: 99%

Motor Imagery EEG Classification Based on Riemannian Sparse Optimization and Dempster-Shafer Fusion of Multi-Time-Frequency Patterns

Jin

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Motor imagery-based brain-computer interfaces (MI-BCIs) features are generally extracted from a wide fixed frequency band and time window of EEG signal. The performance suffers from individual differences in corresponding time to MI tasks. In order to solve the problem, in this study, we propose a novel method named Riemannian sparse optimization and Dempster-Shafer fusion of multi-time-frequency patterns (RSODSF) to enhance the decoding efficiency. First, we effectively combine the Riemannian geometry of the spatial covariance matrix with sparse optimization to extract more robust and distinct features. Second, the Dempster-Shafer theory is introduced and used to fuse each time window after sparse optimization of Riemannian features. Besides, the probabilistic values of the support vector machine (SVM) are obtained and transformed to effectively fuse multiple classifiers to leverage potential soft information of multiple trained SVM. The openaccess BCI Competition IV dataset IIa and Competition III dataset IIIa are employed to evaluate the performance of the proposed RSODSF. It achieves higher average accuracy (89.7% and 96.8%) than state-of-the-art methods. The improvement over the common spatial patterns (SFBCSP) are respectively 9.9% and 12.4% (p<0.01, paired t-test). These results show that our proposed RSODSF method is a promising candidate for the performance improvement of MI-BCI.

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A fuzzy preference-based Dempster-Shafer evidence theory for decision fusion

Cited by 47 publications

References 51 publications

Weighted assignment fusion algorithm of evidence conflict based on Euclidean distance and weighting strategy, and application in the wind turbine system

Weighted assignment fusion algorithm of evidence conflict based on Euclidean distance and weighting strategy, and application in the wind turbine system

Data Fusion Technique for E-Learning Evaluation Based on Evidence Theory

Motor Imagery EEG Classification Based on Riemannian Sparse Optimization and Dempster-Shafer Fusion of Multi-Time-Frequency Patterns

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