Journal ArticleDOI
A new linguistic MCDM method based on multiple-criterion data fusion
TLDR
A new fuzzy evidential MCDM method under uncertain environments is proposed, where the rating of the criteria and the importance weight of the criterion are given by experts' judgments, represented by triangular fuzzy numbers.Abstract:
Research highlights? We proposed a method to deal with MCDM problem under the framework of Demspter-Shafer evidence theory. ? A new fuzzy evidential MCDM method under uncertain environments is proposed. ? The linguistic variables can be transformed into basic probability assignments. ? Data from different criteria can be combined based on the Demspter rule. Multiple-criteria decision-making (MCDM) is concerned with the ranking of decision alternatives based on preference judgements made on decision alternatives over a number of criteria. First, taking advantage of data fusion technology to comprehensively consider each criterion data is a reasonable idea to solve the MCDM problem. Second, in order to efficiently handle uncertain information in the process of decision making, some well developed mathematical tools, such as fuzzy sets theory and Dempster Shafer theory of evidence, are used to deal with MCDM. Based on the two main reasons above, a new fuzzy evidential MCDM method under uncertain environments is proposed. The rating of the criteria and the importance weight of the criteria are given by experts' judgments, represented by triangular fuzzy numbers. Then, the weights are transformed into discounting coefficients and the ratings are transformed into basic probability assignments. The final results can be obtained through the Dempster rule of combination in a simple and straight way. A numerical example to select plant location is used to illustrate the efficiency of the proposed method.read more
Citations
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Supplier selection using AHP methodology extended by D numbers
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Short communication: Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment
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Generalized evidence theory
TL;DR: Wang et al. as discussed by the authors proposed a new theory called generalized evidence theory (GET), which addresses conflict management in an open world, where the frame of discernment is incomplete because of uncertainty and incomplete knowledge.
A Method of Converting Z-number to Classical Fuzzy Number
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References
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Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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Combining belief functions when evidence conflicts
TL;DR: To achieve convergence, this research suggests incorporating average belief into the combining rule, which best solves the normalization problems, but it does not offer convergence toward certainty, nor a probabilistic basis.
Journal ArticleDOI
On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty
Jian-Bo Yang,Dong-Ling Xu +1 more
TL;DR: The fundamental features of the ER approach are investigated and new schemes for weight normalization and basic probability assignments are proposed to enhance the process of aggregating attributes with uncertainty.