Personalized individual semantics in Computing with Words for supporting linguistic Group Decision Making. An Application on Consensus reaching
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AuthorLi, Cong-Cong; Dong, Yucheng; Herrera Triguero, Francisco; Herrera Viedma, Enrique; Martínez, Luis
Computing with words2-tuple linguistic modelSemanticsGroup decision makingPreference relations
Published version: Li, C. C., Dong, Y., Herrera, F., Herrera-Viedma, E., & Martínez, L. (2017). Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching. Information Fusion, 33, 29-40. [http://dx.doi.org/10.1016/j.inffus.2016.04.005]
SponsorshipNational Natural Science Foundation of China 71171160 71571124; Sichuan University skqy201606; European Union (EU) TIN2013-40658-P TIN2015-66524-P
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.