Conflict management based on belief function entropy in sensor fusion

Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

doi:10.1186/s40064-016-2205-6

Cited by 72 publications

(63 citation statements)

References 53 publications

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“…One is to revise the Dempster's combination rule, including Smets's unnormalized combination rule [58], Yager's combination rule [59], Dubois and Prade's disjunctive combination rule [60], etc. The other is to pretreat the bodies of evidences, including Murphy's simple average approach [ [64], etc. Nevertheless, when revising the Dempster's combination rule, some good properties, like the commutativity and associativity are often destructed.…”

Section: Introductionmentioning

confidence: 99%

Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy

2019

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Section: Introductionmentioning

confidence: 99%

Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy

2019

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“…Dempster-Shafer (D-S) evidence theory [48,49] is a powerful tool to handle the uncertainty. It has been widely used in many fields, like risk analysis [52][53][54], controller design [55,56], pattern recognition [57][58][59], fault diagnosis [60][61][62], multiple attribute decision making [63,64] and so on.…”

Section: Introductionmentioning

confidence: 99%

An evidential Markov decision making model

Jiang

2018

Information Sciences

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The sure thing principle and the law of total probability are basic laws in classic probability theory. A disjunction fallacy leads to the violation of these two classical laws. In this paper, an Evidential Markov (EM) decision making model based on Dempster-Shafer (D-S) evidence theory and Markov modelling is proposed to address this issue and model the real human decision-making process. In an evidential framework, the states are extended by introducing an uncertain state which represents the hesitance of a decision maker. The classical Markov model can not produce the disjunction effect, which assumes that a decision has to be certain at one time. However, the state is allowed to be uncertain in the EM model before the final decision is made. An extra uncertainty degree parameter is defined by a belief entropy, named Deng entropy, to assignment the basic probability assignment of the uncertain state, which is the key to predict the disjunction effect. A classical categorization decision-making experiment is used to illustrate the effectiveness and validity of EM model. The disjunction effect can be well predicted and the free parameters are less compared with the existing models.Keywords: Evidential Markov model; Markov decision making model;Dempster-Shafer evidence theory; the sure thing principle; the law of total probability; disjunction effect

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“…Therefore, as a second group, a great number of researchers deal with pre‐processing of BPAs to overcome the counter‐intuitive combination results. For instance, Yuan et al 57 develop a method to address the conflict among the sensor data under uncertainty. They combine the evidence distance and Deng entropy 58 to measure the conflict degree and uncertain information, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

Sensor fusion based on Dempster‐Shafer theory of evidence using a large scale group decision making approach

Köksalmiş

Kabak

2020

Int J Intell Syst

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In group decision making (GDM), the quality of the solution relies primarily on the quality and the expertize of decision makers. At that point, deriving the weights, which reflects their importance or perceived reliability of decision makers, presents as a new challenge. In addition to that, the uncertainty is also a common problem for GDM. These problems are also faced in the sensor fusion problem where information from multiple sources must be aggregated.Therefore, in this study, a large scale GDM approach for sensor fusion is proposed. Since the proposed method is a clustering-based method, it provides acceptable results in the sensor networks consisting of multiple sensors. It can work under uncertainty as a result of converting the raw data obtained from sensors to the basic probability assignments. It also considers the reliability of the sensors clusters by assigning three objective weights. In addition to these objective weights, the proposed method enables to assign subjective weights to integrate supervisors/intelligence analyst experiences and knowledge in the problem field. The applicability and the validity of the proposed method are checked through two real classification data sets: ionosphere and forest type mapping data set. Experiments show that the classification rate is increased significantly when the proposed method is applied to two data sets. Finally, effect of extension parameter, objective weights, reliability threshold, number of clusters and clustering method on the classification rate and the detection probability are examined, and future studies are provided in conclusion.classification, Dempster-Shafer theory of evidence, large scale group decision making, sensor fusion, sensor weighting | INTRODUCTIONAs the complexity of the problem grows with the complexity of the socioeconomic environment, it gets more difficult for a single decision maker to cover all the relevant characteristic of a decision problem. Hence, in the real world, it is required to fuse the group of decision makers' opinion to obtain a feasible and satisfactory decision making result by using group decision making (GDM) approaches. 1,2 Here, the quality of the solution relies primarily on the quality and the expertize of decision makers in the problem area and how to aggregate decision makers' evaluations. 3 Therefore, it is critical to find a good way of aggregating decision makers' opinions whose expertize, background, and knowledge differ. In the literature, it is mostly assumed that the decision makers have the same importance level. 4 However, disregarding the weights of decision makers can cause improper and inaccurate outcomes that cannot be compensated for in the final solution. Therefore, when decision makers do not share similar levels of expertize, background, or knowledge, it is better to assign weights to the decision makers reflecting their importance or perceived reliability. At that point, deriving the weights of decision makers presents as a new challenge. Koksalmis and Kabak 5 presents the objectiv...

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Conflict management based on belief function entropy in sensor fusion

Cited by 72 publications

References 53 publications

Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy

Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy

An evidential Markov decision making model

Sensor fusion based on Dempster‐Shafer theory of evidence using a large scale group decision making approach

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