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Consistency-driven methodology to manage incomplete linguistic preference relation: A perspective based on personalized individual semantics
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Abstract:
The file attached to this record is the author's final peer reviewed version. ; In linguistic decision making problems there may be cased when decision makers will not be able to provide complete linguistic preference relations. However, when estimating unknown linguistic preference values in incomplete preference relations, the existing research approaches ignore the fact that words mean different things for different people, i.e. decision makers have personalized individual semantics (PISs) regarding words. To manage incomplete linguistic preference relations with PISs, in this paper we propose a consistency-driven methodology both to estimate the incomplete linguistic preference values and to obtain the personalized numerical meanings of linguistic values of the different decision makers. The proposed incomplete linguistic preference estimation method combines the characteristic of the personalized representation of decision makers and guarantees the optimum consistency of incomplete linguistic preference relations in the implementation process. Numerical examples and a comparative analysis are included to justify the feasibility of the PISs based incomplete linguistic preference estimation method.
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Keyword:
consistency; incomplete linguistic preference relation; linguistic decision making; Personalized individual semantics
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URL: https://doi.org/10.1109/tcyb.2021.3072147
https://dora.dmu.ac.uk/handle/2086/20804
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Consistency improvement with a feedback recommendation in personalized linguistic group decision making
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Consistency Improvement With a Feedback Recommendation in Personalized Linguistic Group Decision Making
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Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence ...
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Revisiting Fuzzy and Linguistic Decision-Making: Scenarios and Challenges for Wiser Decisions in a Better Way
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Personalized individual semantics in computing with words for supporting linguistic group decision making
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Personalized individual semantics in Computing with Words for supporting linguistic Group Decision Making. An Application on Consensus reaching
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