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Modeling Personalized Individual Semantics and Consensus in Comparative Linguistic Expression Preference Relations With Self-Confidence: An Optimization-Based Approach by Hengjie Zhang; Cong-Cong Li; Yating Liu; Yucheng Dong is a Computer Science article available to read on EtoBox.

What is Modeling Personalized Individual Semantics and Consensus in Comparative Linguistic Expression Preference Relations With Self-Confidence: An Optimization-Based Approach about?

Comparative linguistic expression preference relations (CLEPRs) are an effective tool to represent uncertain opinions of decision makers in group decision-making (GDM). Nevertheless, multiple self-confidence levels are not considered by existing research on CLEPRs. Thus, this study proposes CLEPRs with self-confidence by considering multiple self-confidence levels, and presents a way to measure their consistency level. Meanwhile, personalized individual semantics, indicating that words mean different things for different people, has been highlighted and investigated in the GDM with linguistic assessment information. Considering personalized individual semantics in comparative linguistic expressions, this study proposes an optimization model based on the consistency-driven methodology to assess individual semantics in CLEPRs with self-confidence. Particularly, the personalized individual semantics are described and addressed by setting different numerical scales of linguistic terms for different decision makers. Finally, an optimization-based consensus model was proposed to obtain a consensual collective solution, which seeks to minimize the information loss between the decision mak

Who reads Modeling Personalized Individual Semantics and Consensus in Comparative Linguistic Expression Preference Relations With Self-Confidence: An Optimization-Based Approach?

Author

Hengjie Zhang; Cong-Cong Li; Yating Liu; Yucheng Dong

Publisher

IEEE; Institute of Electrical and Electronics Engineers; Institute of Electrical and Electronics Engineers (IEEE) (ISSN 1063-6706)

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)