Novel approach for measuring the conflict between evidence

被引:6
作者
Bao T.-T. [1 ]
Xie X.-L. [1 ]
Wei Z.-K. [1 ]
机构
[1] Integrated Transport Institute, Dalian Maritime University, Dalian, 116026, Liaoning
来源
Xie, Xin-Lian (xxlian77@yahoo.com) | 2017年 / South China University of Technology卷 / 34期
关键词
Conflicting evidence; Distance between evidence; Entropy; Evidence theory; Uncertainty;
D O I
10.7641/CTA.2017.60275
中图分类号
学科分类号
摘要
To solve the invalidation problems when the evidence theory is used to deal with the cases with highly conflicting evidence, a new method is investigated for measuring and representing the degree of conflict between evidence. First of all, based on the comparison and analysis of some existing evidence distance methods which are the main conflict measures, a new probabilistic-based distance is defined and combined with Jousselme's distance to describe the difference between evidence. Next, because the conception of evidence distance cannot substitute for the definition of evidential conflict completely, a cross entropy of basic probability assignments (BPAs) is proposed based on the idea of fuzzy cross entropy to present the degree of divergence between evidence. Then, in the light of mixing the distance and divergence between evidence by applying Hamacher T-conorm, a new method for measuring the degree of conflict between evidence is put forward. Finally, the numerical examples verify the efficiency of the proposed method. © 2017, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
引用
收藏
页码:41 / 48
页数:7
相关论文
共 25 条
  • [1] Dempster A.P., Upper and lower probabilities induced by a multivalued mapping, Annals of Mathematical Statistics, 38, 2, pp. 325-339, (1967)
  • [2] Zadeh. L.A., A simple view of the dempster-shafer theory of evidence and its implication for the rule of combination, Ai Magazine, 7, 2, pp. 85-90, (1986)
  • [3] Yager R.R., On the dempster-shafer framework and new combination rules, Information Sciences, 41, 2, pp. 93-137, (1987)
  • [4] Dubois D., Prade H., Representation and combination of uncertainty with belief functions and possibility measures, Computational Intelligence, 4, 3, pp. 244-264, (1988)
  • [5] Smarandache F., Dezert J., Tacnet J., Fusion of sources of evidence with different importances and reliabilities, The 13th International Conference on Information Fusion, pp. 1-8, (2010)
  • [6] Murphy C.K., Combining belief functions when evidence conflicts, Decision Support Systems, 29, 1, pp. 1-9, (2000)
  • [7] Yong D., Wenkang S., Zhenfu Z., Et al., Combining belief functions based on distance of evidence, Decision Support Systems, 38, 3, pp. 489-493, (2004)
  • [8] Han D., Deng Y., Han C., Et al., Conflicting evidence combination by using uncertainty degree, Control Theory & Applications, 28, 6, pp. 788-792, (2011)
  • [9] Li S., Yang N., Zhang Y., New approches of distinguishing the evidences importance and allocating the evidences conflict, Systems Engineering Theory and Practice, 33, 7, pp. 1867-1872, (2013)
  • [10] Hu L., Guan X., Deng Y., Et al., Cause-analysis for conflicting evidences in the generalized power space, Control Theory & Applications, 28, 12, pp. 1717-1722, (2011)