A comparative method is proposed to study the influence of evidence distance selection on combination of conflict evidences and the method applies different evidence distance measurements to combination of weighted evidences. Firstly
different evidence distance measurements are used to get similarities of evidences. Then evidence credibilities are calculated using evidence similarities and the averages of evidences are calculated by summing up weighted evidences using the credibilities as weights. Finally
resulting averages are combined. The rationality of these combinations of evidences in different evidence distance measurements is compared to obtain a suitable weights generation in weighted evidence combination. Numerical examples are given to analyze and to compare evidence credibilities
averages of evidences and combination results of evidences. Experimental results and comparisons with other evidence distance measurements show that the evidence distance measurements selected by the proposed method reflect the difference between the evidences more effectively
and the calculated evidence credibilities are more consistent with the actual situation.
关键词
Keywords
references
SHAFER G. A mathematical theory of evidence [M]. Princeton, NJ, USA: Princeton University Press, 1976: 1-10.
ZADEH L A. Review of Shafer's a mathematical theory of evidence [J]. AI Magazine, 1984, 5(3): 81-83.
DENG Yong, SHI Wenkang, ZHU Zhenfu, et al. Combining belief functions based on distance of evidence [J]. Decision Support Systems, 2004, 38(3): 489-493.
XIAO Jianyu, TONG Minming, ZHU Changjie, et al. Improved combination rule of evidence based on pignistic probability distance [J]. Journal of Shanghai Jiaotong University, 2012, 46(4): 636-641.
LIU Zhicheng, QIAO Hui, HE Jiazhou. Combination approach of highly conflicting evidence based on weighted distance of evidence [J]. Computer Engineering Applications, 2014, 50(3): 103-107.
YAGER R R. On the Dempster-Shafer framework and new combination rules [J]. Information Science, 1987, 41(2): 93-137.
DUBOIS D, PRADE H. Representation and combination of uncertainty with belief functions and possibility measures [J]. Computational Intelligence, 1988(4): 244-264.
MURPHY C K. Combining belief functions when evidence conflicts [J]. Decision Support Systems, 2000, 29(1): 1-9.
JOUSSELME A L, PATRICK M. Distance in evidence theory: comprehensive survey and generalization [J]. International Journal of Approximate Reasoning, 2012, 53(2): 118-145.
JOUSSELME A L, GRENIER D, BOSSÉ E. A new distance between two bodies of evidence [J]. Information Fusion, 2001, 2(2): 91-101.
TESSEM B. Approximations for efficient computation in the theory of evidence [J]. Artificial Intelligence, 1993, 61(2): 315-329.
HAN Deqiang, DEZERT J, HAN Chongzhao, et al. New dissimilarity measures in evidence theory [C]∥Proceedings of the 14th International Conference on Information Fusion. Piscataway, NJ, USA: IEEE, 2011: 5977681.
HAN Deqiang, DEZERT J, YANG Yi. Belief interval-based distance measures in the theory of belief functions [J]. IEEE Transactions on Systems Man Cybernetics Systems, 2016, 99: 1-18.
SMETS P. Data fusion in the transferable belief model [C]∥ Proceedings of the 3rd International Conference on Information Fusion. Piscataway, NJ, USA: IEEE Computer Society, 2000: 21-33.
ELOUEDI Z, MELLOULI K, SMETS P. Assessing sensor reliability for multisensor data fusion within the transferable belief model [J]. IEEE Transactions on Systems Man Cybernetics: Part B Cybernetics, 2004, 34(1): 782-787.
XING Qinghua, LIU Fuxian. New combination rule of conflict evidence based on optimized evidence discount [J]. Systems Engineering Electronics, 2009, 31(5): 1158-1161.
FU Yaowen, JIA Yuping, YANG Wei, et al. Sensor dynamic reliability evaluation and evidence discount [J]. Systems Engineering Electronics, 2012, 34(1): 212-216.
DE FERIET J K. Interpretation of membership functions of fuzzy sets in terms of plausibility and belief [J]. Fuzzy Information and Decision Processes, 1982(1): 93-98.