Artificial Intelligence


Intuitionistic Evidence Sets

Authors: Yangxue Li; Yong Deng

Dempster-Shafer evidence theory can express and deal with uncertain and imprecise information well, which satisfies the weaker condition than the Bayes probability theory. The traditional single basic probability assignment only considers the degree of the evidence support the subsets of the frame of discernment. In order to simulate human decision-making processes and any activities requiring human expertise and knowledge, intuitionstic evidence sets (IES) is proposed in this paper. It takes into account not only the degree of the support, but also the degree of non-support. The combination rule of intuitionstic basic probability assignments (IBPAs) also be investigated. Feasibility and effectiveness of the proposed method are illustrated by using an application of multi-criteria group decision making.

Comments: 25 Pages.

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Submission history

[v1] 2018-07-28 07:23:42

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