Title
Reducing Criteria in Multicriteria Group Decision-Making Methods Using Hierarchical Clustering Methods and Fuzzy Ontologies
Abstract
Multicriteria group decision-making environments that have a high number of criterion values can be difficult for the experts to handle. This is due to the fact that the experts have to take too much information into account. Thus, they get lost among all the possibilities and have difficulties making the right decision. In order to solve this problem, we present a novel multicriteria group decision-making method that reduces the initial set of criterion values in an organized way. Hierarchical clustering methods are used in order to generate a new reduced criteria set that can be handled by the experts. Fuzzy ontologies are used as an aid system that stores how much each alternative fulfills each criterion. The presented method makes it possible for the experts to carry out the group decision-making process by focusing on ranking the reduced set of criterion values. As a result, a comfortable decision environment is generated, in which the experts can make decisions by managing a fair amount of information. The aid provided by fuzzy ontologies allows the experts to focus on establishing the importance of the criterion values, leaving the rest to the computational system.
Year
DOI
Venue
2022
10.1109/TFUZZ.2021.3062145
IEEE Transactions on Fuzzy Systems
Keywords
DocType
Volume
Decision support systems,decision making,human–machine interactions,knowledge-based systems
Journal
30
Issue
ISSN
Citations 
6
1063-6706
0
PageRank 
References 
Authors
0.34
43
6
Name
Order
Citations
PageRank
Juan Antonio Morente-Molinera116216.00
Yinglin Wang200.34
Zai-Wu Gong338619.82
Ali Morfeq427517.38
Rami Al-hmouz532319.34
Enrique Herrera-Viedma611.70