scholarly journals An Evidence Combination Rule Based on New Weight Assignment Scheme

2022 ◽  
Author(s):  
yucui wang ◽  
Jian Wang ◽  
Mengjie Huang ◽  
Minghui Wang
Keyword(s):  
Evidence Theory ◽  
Wasserstein Distance ◽  
Combination Rule ◽  
Assignment Method ◽  
Weight Assignment ◽  
Conflicting Evidence ◽  
Evidence Combination ◽  
Reference Index ◽  
The Impact ◽  
Assignment Scheme

Abstract Conflicting evidence and fuzzy evidence have a significant impact on the results of evidence combination in the application of evidence theory. However, the existing weight assignment methods can hardly reflect the significant influence of fuzzy evidence on the combination results. Therefore, a new method for assigning evidence weights and the corresponding combination rule are proposed. The proposed weight assignment method strengthens the consideration of fuzzy evidence and introduces the Wasserstein distance to compute the clarity degree of evidence which is an important reference index for weight assignment in the proposed combination rule and can weaken the effect of ambiguous evidence effectively. In the experiments, it's firstly verified that the impact of fuzzy evidence on the combination results is significant; therefore it should be fully considered in the weight assignment process. Then, the proposed combination rule with new weight assignment method is tested on a set of numerical arithmetic and Iris datasets. Compared with four existing methods, the results show that the proposed method has higher decision accuracy, F1 score, better computational convergence, and more reliable fusion results as well.

Conflicting evidence combination based on Belief Mover’s Distance

2021 ◽  
pp. 1-17
Author(s):  
Shenshen Bai ◽  
Longjie Li ◽  
Xiaoyun Chen
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Weighted Average ◽  
Combination Rule ◽  
The Earth ◽  
Conflicting Evidence ◽  
Evidence Combination ◽  
Final Weight ◽  
The Difference ◽  
New Evidence

The Dempster-Shafer evidence theory has been extensively used in various applications of information fusion owing to its capability in dealing with uncertain modeling and reasoning. However, when meeting highly conflicting evidence, the classical Dempster’s combination rule may give counter-intuitive results. To address this issue, we propose a new method in this work to fuse conflicting evidence. Firstly, a new evidence distance metric, named Belief Mover’s Distance, which is inspired by the Earth Mover’s Distance, is defined to measure the difference between two pieces of evidence. Subsequently, the credibility weight and distance weight of each piece of evidence are computed according to the Belief Mover’s Distance. Then, the final weight of each piece of evidence is generated by unifying these two weights. Finally, the classical Dempster’s rule is employed to fuse the weighted average evidence. Several examples and applications are presented to analyze the performance of the proposed method. Experimental results manifest that the proposed method is remarkably effective in comparison with other methods.

scholarly journals A Multisource Situation Information Fusion Method Based on Dynamic Evidence Combination

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jing Liu ◽  
ChaoWen Chang ◽  
Yuchen Zhang ◽  
Yongwei Wang
Keyword(s):  
Information Fusion ◽  
Detection Rate ◽  
Evidence Theory ◽  
Fusion Method ◽  
False Detection Rate ◽  
False Detection ◽  
Conflicting Evidence ◽  
Evidence Combination ◽  
Fusion Methods ◽  
Fusion Efficiency

To address the problems of fusion efficiency, detection rate (DR), and false detection rate (FDR) that are associated with existing information fusion methods, a multisource information fusion method featuring dynamic evidence combination based on layer clustering and improved evidence theory is proposed in this study. First, the original alerts are hierarchically clustered and conflicting evidence is eliminated. Then, dynamic evidence combination is applied to fuse the condensed alerts, thereby improving the efficiency and accuracy of the fusion. The experimental results show that the proposed method is superior to current fusion methods in terms of fusion efficiency, DR, and FDR.

scholarly journals A Novel Weighted Complex Evidence Combination with Its Application in Multisource Information Fusion

2021 ◽  
Author(s):  
Huaping He ◽  
Liting He ◽  
Fuyuan Xiao
Keyword(s):  
Complex Plane ◽  
Information Fusion ◽  
Distance Function ◽  
Medical Diagnosis ◽  
Evidence Theory ◽  
Fusion Method ◽  
Numerical Example ◽  
Evidence Combination ◽  
The Impact ◽  
Counterintuitive Result

Abstract With the development of evidence theory, classical Dempster-Shafer evidence theory has been extended to complex plane, called complex evidence theory. However, counterintuitive result may occurs in the case when fusing conflicting complex evidences. To address this problem, a new multisource information fusion method is proposed by means of complex evidential distance function. This proposed method can reduce the impact of abnormal complex evidence on the fusion results to better support decision. A numerical example and an application of medical diagnosis verify the feasibility and effectiveness of the proposed fusion method.

scholarly journals Trust evaluation based on evidence theory in online social networks

2018 ◽  
Vol 14 (10) ◽  
pp. 155014771879462 ◽  
Author(s):  
Jian Wang ◽  
Kuoyuan Qiao ◽  
Zhiyong Zhang
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Privacy Preservation ◽  
Evidence Theory ◽  
Trust Evaluation ◽  
Combination Rule ◽  
Privacy Leakage ◽  
Evidence Combination ◽  
Fixed Model ◽  
The Mean

Trust is an important criterion for access control in the field of online social networks privacy preservation. In the present methods, the subjectivity and individualization of the trust is ignored and a fixed model is built for all the users. In fact, different users probably take different trust features into their considerations when making trust decisions. Besides, in the present schemes, only users’ static features are mapped into trust values, without the risk of privacy leakage. In this article, the features that each user cares about when making trust decisions are mined by machine learning to be User-Will. The privacy leakage risk of the evaluated user is estimated through information flow predicting. Then the User-Will and the privacy leakage risk are all mapped into trust evidence to be combined by an improved evidence combination rule of the evidence theory. In the end, several typical methods and the proposed scheme are implemented to compare the performance on dataset Epinions. Our scheme is verified to be more advanced than the others by comparing the F-Score and the Mean Error of the trust evaluation results.

Analysis and Improvement of Combination Rule in D-S Theory

2014 ◽  
Vol 556-562 ◽  
pp. 3930-3934
Author(s):  
Ruo Cheng Wang
Keyword(s):  
Evidence Theory ◽  
Combination Rule ◽  
Effective Combination ◽  
Evidence Combination ◽  
Combination Principle

In this paper, the evidence combination principle of Dempster-Shafer (D-S) evidence theory is analyzed in detail. And the method of evidence combination is improved because of the deficiency of D-S evidence theory. Considering the principle“the minority should be subordinate to the majority”, based on Yager’s rule, we put forward a new effective combination rule which works more reasonably.

A Comparative Study of Evidence Theories in the Modeling, Analysis, and Design of Engineering Systems

2013 ◽  
Vol 135 (6) ◽  
Author(s):  
S. S. Rao ◽  
K. K. Annamdas
Keyword(s):  
Evidence Theory ◽  
Engineering Systems ◽  
Combination Rule ◽  
Multiple Sources ◽  
Combination Rules ◽  
Analysis And Design ◽  
Modeling Analysis ◽  
Evidence Combination ◽  
Different Types ◽  
Shafer Theory

The application of different types of evidence theories in the modeling, analysis and design of engineering systems is explored. In most studies dealing with evidence theory, the Dempster–Shafer theory (DST) has been used as the framework not only for the characterization and representation of uncertainty but also for combining evidence. The versatility of the theory is the motivation for selecting DST to represent and combine different types of evidence obtained from multiple sources. In this work, five evidence combination rules, namely, Dempster–Shafer, Yager, Inagaki, Zhang, and Murrphy combination rules, are considered. The limitations and sensitivity of the DST rule in the case of conflicting evidence are illustrated with examples. The application of all the five evidence combination rules for the modeling, analysis and design of engineering systems is illustrated using a power plant failure example and a welded beam problem. The aim is to understand the basic characteristics of each rule and develop preliminary guidelines or criteria for selecting an evidence combination rule that is most appropriate based on the nature and characteristics of the available evidence. Since this work is the first one aimed at developing the guidelines or criteria for selecting the most suitable evidence combination rule, further studies are required to refine the guidelines and criteria developed in this work.

scholarly journals Multisensor Fusion Method Based on the Belief Entropy and DS Evidence Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Xiaojing Fan ◽  
Yinjing Guo ◽  
Yuanyuan Ju ◽  
Jiankang Bao ◽  
Wenhong Lyu
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Case Analysis ◽  
Entire Body ◽  
Combination Rule ◽  
Target Concentration ◽  
Conflicting Evidence ◽  
Multisensor Information Fusion ◽  
Belief Entropy ◽  
Weighted Factor

The Dempster–Shafer evidence theory has been widely applied in multisensor information fusion. Nevertheless, illogical results may occur when fusing highly conflicting evidence. To solve this problem, a new method of the grouping of evidence is proposed in this paper. This method uses a combination of the belief entropy and the degree of conflict of the evidence as the judgment rule and divides the entire body of evidence into two separate groups. For the grouped evidence, both the credibility weighted factor based on the belief entropy function and the support weighted factor based on the Jousselme distance function are taken into consideration. The two determined weighted factors are integrated to adjust the evidence before applying the DS combination rule. Numerical examples are provided to demonstrate the theoretical feasibility and rationality of the proposed method. The fusion results indicate that the proposed method is more accurate than the compared algorithms in handling the paradoxes. A decision-making case analysis of the biological system is performed to validate the practical applicability of the proposed method. The results confirm that the proposed method has the highest belief degree of the target concentration (50.98%) and has superior accuracy compared to other related methods.

scholarly journals An improved conflicting-evidence combination method based on the redistribution of the basic probability assignment

2021 ◽  
Author(s):  
Zezheng Yan ◽  
Hanping Zhao ◽  
Xiaowen Mei
Keyword(s):  
Evidence Theory ◽  
Combination Method ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Combination Methods ◽  
Conflicting Evidence ◽  
Basic Probability ◽  
The Right ◽  
Conflict Intensity ◽  
Dempster’S Rule Of Combination

AbstractDempster–Shafer evidence theory is widely applied in various fields related to information fusion. However, the results are counterintuitive when highly conflicting evidence is fused with Dempster’s rule of combination. Many improved combination methods have been developed to address conflicting evidence. Nevertheless, all of these approaches have inherent flaws. To solve the existing counterintuitive problem more effectively and less conservatively, an improved combination method for conflicting evidence based on the redistribution of the basic probability assignment is proposed. First, the conflict intensity and the unreliability of the evidence are calculated based on the consistency degree, conflict degree and similarity coefficient among the evidence. Second, the redistribution equation of the basic probability assignment is constructed based on the unreliability and conflict intensity, which realizes the redistribution of the basic probability assignment. Third, to avoid excessive redistribution of the basic probability assignment, the precision degree of the evidence obtained by information entropy is used as the correction factor to modify the basic probability assignment for the second time. Finally, Dempster’s rule of combination is used to fuse the modified basic probability assignment. Several different types of examples and actual data sets are given to illustrate the effectiveness and potential of the proposed method. Furthermore, the comparative analysis reveals the proposed method to be better at obtaining the right results than other related methods.

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