Dempster–Shafer evidence theory is widely used in the information fusion field for its effectivity in representing and handling uncertain information. However, applications of Dempster rule in combining multiple conflicting evidence often cause counterintuitive results. One of the existing researches on conflict is based on the similarity of evidence. However, due to the fact that computational complexity of the existing methods is large, it is difficult to meet the real-time requirements of systems. Therefore, new effective methods with acceptable expense should be explored. In this article, following the idea of modifying the source model of evidence, a new method based on DEMATEL is proposed to take the weight of each evidence into consideration. First, the total-relation matrix is determined by the similarity among evidence. Second, prominence and importance are calculated. Finally, the weighted average combination result can be obtained based on Dempster’s rule of combination. Numerical examples are used to demonstrate that the proposed model is efficient to both deals with conflicting evidence and reduce computational complexity.
Dempster–Shafer evidence theory Conflict management DEMATEL Belief function
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The work is partially supported by National Natural Science Foundation of China (Grant Nos. 61573290, 61503237). The authors greatly appreciate the reviews’ suggestions and the editor’s encouragement.
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Conflict of interest
We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.
Altınçay H (2006) On the independence requirement in dempster-shafer theory for combining classifiers providing statistical evidence. Appl Intell 25(1):73–90CrossRefzbMATHGoogle Scholar
Deng X, Jiang W (2018) Dependence assessment in human reliability analysis using an evidential network approach extended by belief rules and uncertainty measures. Ann Nucl Energy 117:183–193CrossRefGoogle Scholar
Deng X, Jiang W (2018) An evidential axiomatic design approach for decision making using the evaluation of belief structure satisfaction to uncertain target values. Int J Intell Syst 33(1):15–32CrossRefGoogle Scholar
Deng Y, Shi WK, Zhu ZZ, Liu Q (2004) Combining belief functions based on distance of evidence. Decis Support Syst 38(3):489–493CrossRefGoogle Scholar
Gabus A, Fontela E (1972) World Problems, an Invitation to further thought within the framework of DEMATEL. Battelle Geneva Research Center, GenevaGoogle Scholar
Haenni A (2002) Are alternatives to dempster’s rule of combination real alternatives: Comments on about the belief function combination and the conflict management problem-lefevre, et al. Inf Fusion 3(3):237–239Google Scholar
Haenni R (2005) Shedding new light on zadeh’s criticism of dempster’s rule of combination. In: 2005 7th international conference on information fusion, vol 2. IEEE, pp 6Google Scholar
Jiang W, Wang S, Liu X, Zheng H, Wei B (2017) Evidence conflict measure based on OWA operator in open world. PloS ONE 12(5):e0177,828Google Scholar
Jiang W, Wei B (2018) Intuitionistic fuzzy evidential power aggregation operator and its application in multiple criteria decision-making. Int J Syst Sci 49(3):582–594MathSciNetCrossRefzbMATHGoogle Scholar
Jiang W, Wei B, Liu X, Li X, Zheng H (2018) Intuitionistic fuzzy power aggregation operator based on entropy and its application in decision making. Int J Intell Syst 33(1):49–67CrossRefGoogle Scholar
Jiang W, Wei B, Qin X, Zhan J, Tang Y (2016) Sensor data fusion based on a new conflict measure. Math Prob Eng 2016:11MathSciNetGoogle Scholar
Jousselme AL, Grenier D, Bosse E (2001) A new distance between two bodies of evidence. Inf Fusion 2(2):91–101CrossRefGoogle Scholar
Kang B, Chhipi-Shrestha G, Deng Y, Hewage K, Sadiq R (2018) Stable strategies analysis based on the utility of Z-number in the evolutionary games. Appl Math Comput 324:202–217MathSciNetGoogle Scholar
Lo CC, Chen WJ (2012) A hybrid information security risk assessment procedure considering interdependences between controls. Expert Syst Appl 39(1):247–257CrossRefGoogle Scholar
Ma J, Liu W, Miller P, Zhou H (2016) An evidential fusion approach for gender profiling. Inf Sci 333:10–20CrossRefGoogle Scholar
Mo H, Deng Y (2016) A new aggregating operator in linguistic decision making based on D numbers. Int J Uncertain Fuzziness Know Based Syst 24(6):831–846CrossRefzbMATHGoogle Scholar
Mönks U, Dörksen H, Lohweg V, Hübner M (2016) Information fusion of conflicting input data. Sensors 16(11):1798CrossRefGoogle Scholar
Murphy C (2000) Combining belief functions when evidence conflicts. Decis Support Syst 29(1):1–9CrossRefGoogle Scholar
Perez A, Tabia H, Declercq D, Zanotti A (2016) Using the conflict in dempstershafer evidence theory as a rejection criterion in classifier output combination for 3d human action recognition. Image Vis Comput 55:149–157CrossRefGoogle Scholar
Shafer G (1976) A mathematical theory of evidence, vol 1. Princeton University Press, PrincetonzbMATHGoogle Scholar
Tseng ML (2009) A causal and effect decision making model of service quality expectation using grey-fuzzy dematel approach. Expert Syst Appl 36(4):7738–7748CrossRefGoogle Scholar
Tzeng GH, Chiang CH, Li CW (2007) Evaluating intertwined effects in e-learning programs: a novel hybrid mcdm model based on factor analysis and dematel. Expert Syst Appl 32(4):1028–1044CrossRefGoogle Scholar
Wu HH, Chen HK, Shieh JI (2010) Evaluating performance criteria of employment service outreach program personnel by dematel method. Expert Syst Appl 37(7):5219–5223CrossRefGoogle Scholar