Sequential weighted combination for unreliable evidence based on evidence variance

Authors:
Deqiang Han;Yong Deng;Chongzhao Han
Affiliations:
MOE KLINNS Lab, Institute of Integrated Automation, Xi'an Jiaotong University, Xi'an 710049, China;School of Electronics and Information Technology, Shanghai Jiaotong University, Shanghai 200240, China;MOE KLINNS Lab, Institute of Integrated Automation, Xi'an Jiaotong University, Xi'an 710049, China
Venue:
Decision Support Systems
Year:
2013

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Abstract

Dempster-Shafer evidence theory is a powerful tool in uncertainty reasoning and decision-making. However counter-intuitive results can be encountered when unreliable bodies of evidence are combined by using Dempster's rule of combination in some cases. In this paper, a novel sequential evidence combination approach is proposed based on the weighted modification of bodies of evidence according to our proposed variances of evidence sequences. Experimental results show that the proposed approach is rational and effective.