Improved Base Belief Function-Based Conflict Data Fusion Approach Considering Belief Entropy in the Evidence Theory

Abstract

Due to the nature of the Dempster combination rule, it may produce results contrary to intuition. Therefore, an improved method for conflict evidence fusion is proposed. In this paper, the belief entropy in D–S theory is used to measure the uncertainty in each evidence. First, the initial belief degree is constructed by using an improved base belief function. Then, the information volume of each evidence group is obtained through calculating the belief entropy which can modify the belief degree to get the final evidence that is more reasonable. Using the Dempster combination rule can get the final result after evidence modification, which is helpful to solve the conflict data fusion problems. The rationality and validity of the proposed method are verified by numerical examples and applications of the proposed method in a classification data set.

Measuring the Uncertainty in the Original and Negation of Evidence Using Belief Entropy for Conflict Data Fusion

“…Therefore, the fault diagnosis experiment results show that the fault type is F2, and the effectiveness of the proposed method is also verified. In addition, according to Table 12 , Table 13 and Table 14 and Figure 3 , we can find that the method proposed in this paper has higher confidence for F2 than the data fusion method proposed in the literature [ 50 , 51 , 54 ], and it effectively avoids the confidence dispersion existing in the literature [ 50 , 51 ], which leads to the problem that is not conducive to decision making. This is because the proposed method obtains more uncertain information through the negation of BPA, improves the accuracy of information processing, and reduces the loss of information.…”

“…Therefore, according to the experimental results of this method, even if the second group of evidence has a big conflict with other evidence, we can still infer that A is the correct target, which is in line with our intuition and proves the effectiveness of this method. At the same time, in comparison with other fusion methods, we found that the methods of Ni et al’s [ 50 ] and Gan et al’s [ 51 ] carried out uncertainty measurement and data fusion for the subset of propositions that are not needed for decision-making. The process is more cumbersome and will lead to dispersal of confidence.…”

A New Total Uncertainty Measure from A Perspective of Maximum Entropy Requirement

“…It is widely used in fault diagnosis [11][12][13][14], decision-making [15,16], risk assessment [17], and so on. Many studies in recent years have focused on conflict resolution [18][19][20], evidence revision [21], combination rules [22][23][24][25], and information volume [26,27]. Many methods about uncertainty quantification have also been proposed [28].…”

A Novel Conflict Management Method Based on Uncertainty of Evidence and Reinforcement Learning for Multi-Sensor Information Fusion

“…Jiang and Wei et al [ 42 ] proposed a weighted average method based on the credibility of evidence to deal with high-conflict evidence. Ni et al [ 43 ] presented an improved conflict evidence fusion method, in which the degree of uncertainty of evidence was used to design the weight coefficient of each evidence.…”