On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
Combining belief functions based on distance of evidence
Decision Support Systems
Fusion rules for merging uncertain information
Information Fusion
Analyzing the combination of conflicting belief functions
Information Fusion
A novel conflict reassignment method based on grey relational analysis (GRA)
Pattern Recognition Letters
A DS-AHP approach for multi-attribute decision making problem with incomplete information
Expert Systems with Applications: An International Journal
Comparing approximate reasoning and probabilistic reasoning using the Dempster--Shafer framework
International Journal of Approximate Reasoning
Evidence relationship matrix and its application to d-s evidence theory for information fusion
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Uncertainty representation using fuzzy measures
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Some strategies for explanations in evidential reasoning
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
| Hi-index | 12.05 |
Recent studies on the combination rule of D-S evidence theory can be summarized as two categories, i.e. the methods based on modification for Dempster rule, and the methods based on correction to original evidence sources. However, both of them fail to deal with the role of cardinality of focal elements in process of combination, leading generally to the imperfect final results which are hard to show the inherent differences between different bodies of evidence. In this paper, we propose a combination rule based on the strategy of cross merging between evidences, a newly developed methodology which can better reflect the focusing degree of focal elements between bodies of evidence, and make final combination results more succinct, reasonable and valid. Throughout the process of combination, such global information as reliability, focusing weights, and expected support of evidences is thoroughly taken into account. In addition, an effort is made to allocate evidential conflict in proportion only among combination propositions in order to refine combination results and reduce their decision range. A numerical example and its modified version demonstrate that the presented approach has good adaptability to the evidences in accord or with high conflict, and significantly outperforms other combination methods of a similar kind.