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Personalized individual semantics in Computing with Words for supporting linguistic Group Decision Making. An Application on Consensus reaching

TítuloPersonalized individual semantics in Computing with Words for supporting linguistic Group Decision Making. An Application on Consensus reaching
Tipo de publicaciónRevista Internacional
Año de publicación2017
AutoresC-C. Li, Y. Dong, F. Herrera, E. Herrera-Viedma and L. Martínez
RevistaInformation Fusion
Volumen33
Incidencia1
Paginación29-40
ISSN Number1566-2535
Palabras clavepreference relations
Resumen

Abstract In group decision making (GDM) dealing with Computing with Words (CW) has been highlighted the importance of the statement, words mean different things for different people, because of its influence in the final decision. Different proposals that either grouping such different meanings (uncertainty) to provide one representation for all people or use multi-granular linguistic term sets with the semantics of each granularity, have been developed and applied in the specialized literature. Despite these models are quite useful they do not model individually yet the different meanings of each person when he/she elicits linguistic information. Hence, in this paper a personalized individual semantics (PIS) model is proposed to personalize individual semantics by means of an interval numerical scale and the 2-tuple linguistic model. Specifically, a consistency-driven optimization-based model to obtain and represent the \{PIS\} is introduced. A new \{CW\} framework based on the 2-tuple linguistic model is then defined, such a \{CW\} framework allows us to deal with \{PIS\} to facilitate \{CW\} keeping the idea that words mean different things to different people. In order to justify the feasibility and validity of the \{PIS\} model, it is applied to solve linguistic \{GDM\} problems with a consensus reaching process.

URLhttp://www.sciencedirect.com/science/article/pii/S1566253516300227
DOI10.1016/j.inffus.2016.04.005
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