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Consistency-Driven Methodology to Manage Incomplete Linguistic Preference Relation: A Perspective Based on
IEEE Transactions on Cybernetics
|June 16, 2021
Summary
This study introduces a new method for linguistic decision-making, addressing incomplete preference relations by accounting for personalized individual semantics (PISs). It estimates missing values while ensuring optimal consistency, improving decision accuracy.
Area of Science:
- Decision Sciences
- Linguistics
- Artificial Intelligence
Background:
- Decision-making often involves incomplete linguistic preference relations.
- Existing methods overlook personalized individual semantics (PISs), where word meanings vary among decision-makers.
- This gap leads to inaccuracies in estimating unknown linguistic preference values.
Purpose of the Study:
- To propose a consistency-driven methodology for managing incomplete linguistic preference relations with PISs.
- To estimate incomplete linguistic preference values effectively.
- To derive personalized numerical meanings of linguistic values for different decision-makers.
Main Methods:
- Developed a novel consistency-driven methodology.
- Integrated personalized representation of decision-makers' semantics.
- Ensured optimal consistency in incomplete linguistic preference relations.
Main Results:
- The proposed method successfully estimates incomplete linguistic preference values.
- Personalized numerical meanings of linguistic values were obtained.
- Demonstrated feasibility through numerical examples and comparative analysis.
Conclusions:
- The PISs-based incomplete linguistic preference estimation method is feasible and effective.
- Accounting for individual semantics enhances the accuracy of linguistic decision-making.
- This approach offers a more realistic way to handle incomplete preference data.
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