Personalized individual semantics in Computing with Words for supporting linguistic Group Decision Making. An Application on Consensus reaching
Metadata
Show full item recordAuthor
Li, Cong-Cong; Dong, Yucheng; Herrera Triguero, Francisco; Herrera Viedma, Enrique; Martínez, LuisEditorial
Elsevier
Materia
Computing with words 2-tuple linguistic model Semantics Group decision making Preference relations
Date
2016-03-17Referencia bibliográfica
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]
Sponsorship
National Natural Science Foundation of China 71171160 71571124; Sichuan University skqy201606; European Union (EU) TIN2013-40658-P TIN2015-66524-PAbstract
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.