Authors:
Ana Tapia-Rosero
1
;
Antoon Bronselaer
2
and
Guy De Tré
2
Affiliations:
1
Escuela Superior Politécnica del Litoral and Ghent University, Ecuador
;
2
Ghent University, Belgium
Keyword(s):
Membership Function, Similarity, Cluster, Fusion, Decision-making.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Fuzzy Systems
;
Fuzzy Systems Design, Modeling and Control
;
Mathematical Foundations: Fuzzy Set Theory and Fuzzy Logic
;
Pattern Recognition: Fuzzy Clustering and Classifiers
;
Soft Computing
;
Soft Computing and Intelligent Agents
Abstract:
In this paper, we propose a method to group similar membership functions, each of them representing the opinion of an expert, to obtain a resulting membership function that represents alike opinions among a group. The similarity is based on the shape characteristics of membership functions used to represent the expert opinions on a specific criterion. There are several applications for the proposed method which include group decision making, suitability analysis and consensual processes. In each of these applications diverse points of view are present. The goals of the method are to detect similar membership functions, to establish a manner that allows the selection of representative opinions and to obtain a result membership function that represents a specific trend or a suitable concept for a group of similar membership functions. Our approach is based on soft computing techniques, considering expert preferences as a matter of degree, including a novel method to process similar opi
nions with more ease.
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