Uncertainty-Based Information: Elements of Generalized Information Theory
Uncertainty-Based Information: Elements of Generalized Information Theory
On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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During the process of fusing multi-source information, a challenging problem is how to deal effectively with the available data and information that are vague, imprecise and uncertain. Vague sets are suitable for accurately describing the uncertain information. Dempster-Shafer (D-S) theory is a promising method for the combination of evidence obtained from different source. A new approach is proposed by combining vague sets and D-S theory as there are some similarities between these two theories. By the use of vague sets, characteristics of the factors related to evaluate a component could be described in crisp form and can be represented using belief and plausibility functions defined in D-S theory. Then they are combined using an enhanced weight normalized combination algorithm based on D-S evidence combination rule to reduce the incompleteness and uncertainty. An example is conducted to demonstrate the effectiveness of the proposed method. According to its firm mathematical foundation, the proposed approach can be applied to any bodies of multi-source information fusion without changing the recursive combination algorithm.