Fuzzy algorithm for group decision making with participants having finite discriminating abilities
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Mass function derivation and combination in multivariate data spaces
Information Sciences: an International Journal
A linguistic CMAC equivalent to a linguistic decision tree for classification
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Decision making with dynamically arriving information
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Fuzzy multiple criteria hierarchical group decision-making based on interval type-2 fuzzy sets
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
On the dynamic evidential reasoning algorithm for fault prediction
Expert Systems with Applications: An International Journal
An empirical test of the evidential reasoning approach's synthesis axioms
Expert Systems with Applications: An International Journal
Evidential reasoning rule for evidence combination
Artificial Intelligence
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In multiple-attribute decision making (MADM) problems, one often needs to deal with decision information with uncertainty. During the last decade, Yang and Singh (1994) have proposed and developed an evidential reasoning (ER) approach to deal with such MADM problems. Essentially, this approach is based on an evaluation analysis model and Dempster's rule of combination in the Dempster-Shafer (D-S) theory of evidence. This paper reanalyzes the ER approach explicitly in terms of D-S theory and then proposes a general scheme of attribute aggregation in MADM under uncertainty. In the spirit of such a reanalysis, previous ER algorithms are reviewed and two other aggregation schemes are discussed. Theoretically, it is shown that new aggregation schemes also satisfy the synthesis axioms, which have been recently proposed by Yang and Xu (2002) for which any rational aggregation process should grant. A numerical example traditionally examined in published sources on the ER approach is used to illustrate the discussed techniques