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 Research on Improvement of the Combination Method for Conflicting Evidence Based on Historical Data

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 762
Author(s):  
Shuai Yuan ◽  
Honglei Wang
Keyword(s):  
Decision Rule ◽  
Historical Data ◽  
Evidence Theory ◽  
Weighted Average ◽  
Combination Method ◽  
Final Decision ◽  
Evidence Based ◽  
Combination Methods ◽  
Conflicting Evidence ◽  
The Difference

In a multi-sensor system, due to the difference of performance of sensors and the environment in which the sensor collects evidence, evidence collected will be highly conflicting, which leads to the failure of D-S evidence theory. The current research on combination methods of conflicting evidence focuses on eliminating the problem of "Zadeh paradox" brought by conflicting evidence, but do not distinguish the evidence from different sources effectively. In this paper, the credibility of each piece of evidence to be combined is weighted based on historical data, and the modified evidence is obtained by weighted average. Then the final result is obtained by combining the modified evidence using D-S evidence theory, and the improved decision rule is used for the final decision. After the decision, the system updates and stores the historical data based on actual results. The improved decision rule can solve the problem that the system cannot make a decision when there are two or more propositions corresponding to the maximum support in the final combination result. This method satisfies commutative law and associative law, so it has the symmetry that can meet the needs of the combination of time-domain evidence. Numerical examples show that the combination method of conflict evidence based on historical data can not only solve the problem of “Zadeh paradox”, but also obtain more reasonable results.

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 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.

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 Research on Improved DS Evidence Theory

2021 ◽  
Author(s):  
Zhen Lin ◽  
Jinye Xie
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Weight Matrix ◽  
Fusion Model ◽  
Conflicting Evidence ◽  
Evidence Source ◽  
Sensor Information ◽  
Focal Element ◽  
New Evidence

Abstract In view of the lack of effective information fusion model for heterogeneous multi-sensor, an improved DS evidence theory algorithm is designed to fuse heterogeneous multi-sensor information. The algorithm first introduces the compatibility coefficient to characterize the compatibility between the evidence, obtains the weight matrix of each proposition, and then redistributes the BPA of each focal element to obtain a new evidence source. Then, the concept of credibility is introduced, and the synthetic rules are improved by using the credibility of evidence and the average support of evidence focal elements, so as to obtain the fusion results. The results show that compared with other algorithms, this algorithm can solve the problems of DS evidence theory in dealing with highly conflicting evidence to a certain extent, and the fused results are more reasonable and converge faster.

scholarly journals Research on improved evidence theory based on multi-sensor information fusion

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhen Lin ◽  
Jinye Xie
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Fusion Model ◽  
Conflicting Evidence ◽  
Evidence Source ◽  
Basic Probability ◽  
Sensor Information ◽  
Focal Element ◽  
New Evidence ◽  
Evidence Credibility

AbstractIn view of the lack of effective information fusion model for heterogeneous multi-sensor, an improved Dempster/Shafer (DS) evidence theory algorithm is designed to fuse heterogeneous multi-sensor information. The algorithm first introduces the compatibility coefficient to characterize the compatibility between the evidence, obtains the weight matrix of each proposition, and then redistributes the basic probability distribution of each focal element to obtain a new evidence source. Then the concept of credibility is introduced, and the average support of evidence credibility and evidence focal element is used to improve the synthesis rule, so as to obtain the fusion result. Compared with other algorithms, the proposed algorithm can solve the problems existing in DS evidence theory when dealing with highly conflicting evidence to a certain extent, and the fusion results are more reasonable and the convergence speed is faster.

scholarly journals A Self-Adaptive Combination Method in Evidence Theory Based on the Power Pignistic Probability Distance

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 526
Author(s):  
Jian Wang ◽  
Jing-wei Zhu ◽  
Yafei Song
Keyword(s):  
Evidence Theory ◽  
Weighted Average ◽  
Combination Method ◽  
Multiple Sources ◽  
Adaptive Combination ◽  
Conflicting Evidence ◽  
Different Types ◽  
Fusion Methods ◽  
Temporal And Spatial ◽  
Probability Distance

Existing methods employed for combining temporal and spatial evidence derived from multiple sources into a single coherent description of objects and their environments lack versatility in various applications such as multi-sensor target recognition. This is addressed in the present study by proposing an adaptive evidence fusion method based on the power pignistic probability distance. This method classifies evidence sets into non-conflicting and conflicting evidence sets based on the maximum power pignistic probability distance obtained between evidence pairs in the evidence set. Non-conflicting evidence sets are fused using Dempster’s rule, while conflicting evidence sets are fused using a weighted average combination method based on the power pignistic probability distance. The superior evidence fusion performance of the proposed method is demonstrated by comparisons with the performances of seven other fusion methods based on numerical examples with four different evidence conflict scenarios. The results show that the method proposed in this paper not only can properly fuse different types of evidence, but also provides an excellent focus on the components of evidence sets with high confidence, which is conducive to timely and accurate decisions.

scholarly journals Research on Information Fusion for Machine Potential Fault Operation and Maintenance

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 375
Author(s):  
Wei Xu ◽  
Yi Wan ◽  
Tian-Yu Zuo ◽  
Xin-Mei Sha
Keyword(s):  
Decision Making ◽  
Information Fusion ◽  
Decision Model ◽  
Subjective Experience ◽  
Evidence Theory ◽  
Sensor Technology ◽  
Operation And Maintenance ◽  
Symmetric Structure ◽  
Asymmetric Data ◽  
The Difference

In recent years, the development of sensor technology in industry has profoundly changed the way of operation and management in manufacturing enterprises. Due to the popularization and promotion of sensors, the maintenance of machines on the production line are also changing from the subjective experience-based machine maintenance to objective data-driven maintenance decision-making. Therefore, more and more data decision model has been developed through AI technology and intelligence algorithms. Equally important, the information fusion between decision results, which got by data decision model, has also received widespread attention. Information fusion is performed on symmetric data structures. The asymmetric data under the symmetric structure leads to the difference in information fusion results. Therefore, fully considering the potential differences of asymmetric data under a symmetric structure is an important content of information fusion. In view of the above, this paper studies how to make information fusion between different decision results through the framework of D-S evidence theory and discusses the deficiency of D-S evidence theory in detail. Based on D-S evidence theory, then a comprehensive evidence method for information fusion is proposed in this paper. We illustrate the rationality and effectiveness of our method through analysis of experiment case. And, this method is applied to a real case from industry. Finally, the irrationality of the traditional D-S method in the comprehensive decision-making results of machine operation and maintenance was solved by our novel method.

scholarly journals A Novel Evidence Conflict Measurement for Multi-Sensor Data Fusion Based on the Evidence Distance and Evidence Angle

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 381 ◽  
Author(s):  
Zhan Deng ◽  
Jianyu Wang
Keyword(s):  
Measurement Method ◽  
Evidence Theory ◽  
Weighted Average ◽  
Uncertainty Modeling ◽  
New Method ◽  
Sensor Data ◽  
Mutual Support ◽  
Combination Rule ◽  
Practical Applications ◽  
Multi Sensor Data Fusion

As an important method for uncertainty modeling, Dempster–Shafer (DS) evidence theory has been widely used in practical applications. However, the results turned out to be almost counter-intuitive when fusing the different sources of highly conflicting evidence with Dempster’s combination rule. In previous researches, most of them were mainly dependent on the conflict measurement method between the evidence represented by the evidence distance. However, it is inaccurate to characterize the evidence conflict only through the evidence distance. To address this issue, we comprehensively consider the impacts of the evidence distance and evidence angle on conflicts in this paper, and propose a new method based on the mutual support degree between the evidence to characterize the evidence conflict. First, the Hellinger distance measurement method is proposed to measure the distance between the evidence, and the sine value of the Pignistic vector angle is used to characterize the angle between the evidence. The evidence distance indicates the dissimilarity between the evidence, and the evidence angle represents the inconsistency between the evidence. Next, two methods are combined to get a new method for measuring the mutual support degree between the evidence. Afterward, the weight of each evidence is determined by using the mutual support degree between the evidence. Then, the weights of each evidence are utilized to modify the original evidence to achieve the weighted average evidence. Finally, Dempster’s combination rule is used for fusion. Some numerical examples are given to illustrate the effectiveness and reasonability for the proposed method.

scholarly journals An Extended Base Belief Function in Dempster–Shafer Evidence Theory and Its Application in Conflict Data Fusion

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2137
Author(s):  
Dingyi Gan ◽  
Bin Yang ◽  
Yongchuan Tang
Keyword(s):  
Data Fusion ◽  
Data Management ◽  
Information Fusion ◽  
Evidence Theory ◽  
Belief Function ◽  
Mass Function ◽  
Combination Rule ◽  
Basic Probability ◽  
Belief Entropy ◽  
Management Method

The Dempster–Shafer evidence theory has been widely applied in the field of information fusion. However, when the collected evidence data are highly conflicting, the Dempster combination rule (DCR) fails to produce intuitive results most of the time. In order to solve this problem, the base belief function is proposed to modify the basic probability assignment (BPA) in the exhaustive frame of discernment (FOD). However, in the non-exhaustive FOD, the mass function value of the empty set is nonzero, which makes the base belief function no longer applicable. In this paper, considering the influence of the size of the FOD and the mass function value of the empty set, a new belief function named the extended base belief function (EBBF) is proposed. This method can modify the BPA in the non-exhaustive FOD and obtain intuitive fusion results by taking into account the characteristics of the non-exhaustive FOD. In addition, the EBBF can degenerate into the base belief function in the exhaustive FOD. At the same time, by calculating the belief entropy of the modified BPA, we find that the value of belief entropy is higher than before. Belief entropy is used to measure the uncertainty of information, which can show the conflict more intuitively. The increase of the value of entropy belief is the consequence of conflict. This paper also designs an improved conflict data management method based on the EBBF to verify the rationality and effectiveness of the proposed method.

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