Paper Info
Title
An Evidential Aggregation Method of Intuitionistic Fuzzy Sets Based on Belief Entropy.
Abstract
Intuitionistic fuzzy sets (IFSs) are essential in the multi-criteria decision making (MCDM) under uncertain environment. However, how to reasonably aggregate them with considering the uncertainty contained in the IFSs is still an open issue. In this paper, a new method is proposed to solve such a problem based on the Dempster-Shafer evidence theory, belief entropy, and the weighted ordered weighted averaging (WOWA) operator. One of the advantages of the presented model is that the uncertainty contained in the IFSs is effectively modeled based on belief entropy and the conversion from the IFS to Dempster-Shafer evidence theory. In the framework of evidence theory, the uncertain information contained in the IFSs can be embodied effectively. Then, the belief entropy is calculated to determine the certainty weights for each IFS. With the various definitions of the regular increasing monotone (RIM) quantifier Q function, the preference relationship of a decision maker is considered. A numerical example is shown to illustrate the feasibility and effectiveness of the proposed method.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2918707
IEEE ACCESS
Keywords
Field
DocType
Intuitionistic fuzzy sets,multi-criteria decision making,Dempster-Shafer evidence theory,belief entropy,weighted ordered weighted averaging operator,preference
Mathematical optimization,Multiple-criteria decision analysis,Certainty,Computer science,Fuzzy set,Operator (computer programming),Q-function,Monotone polygon,Decision maker,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Zeyi Liu100.68
Fuyuan Xiao220119.11