A New Fusion Method of Conflicting Interval Evidence Based on the Similarity Measure of Evidence

Based on the similarity measure of evidence, a new method for combining conflicting interval evidence is proposed. Firstly, interval evidence can be transformed into interval-valued Pignistic probability by using the defined extended Pignistic probability function. Using the normalized Euclidean distance of interval-valued fuzzy sets, the similarity between Pignistic probabilities of interval evidence are obtained, and similarity measure matrix can be constructed, from which the credibility degrees (weights) of interval evidence can be got. Secondly, based on the credibility degrees, new interval evidence can be obtained by modified and weightedly averaging the original interval evidence. Using Demspter interval evidence combination rule, the fusion result can be obtained by combining the new interval evidence. The proposed method can effectively eliminate the effect of highly conflicting interval evidence in combination so as to reduce the width of combined interval evidence. Therefore the uncertainty of decision-making can be decreased. Finally, in classical numerical examples, compared with the fused results by directly using Demspter interval evidence combination rule, the combined results by using this proposed method are more rational and reliable.