Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching
By Congcong Li, Yucheng Dong, Francisco Herrera, Enrique Herrera-Viedma, Luis Martínez-López. Information Fusion, 2017
352 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/b5e395881a3792fce5315afc599277577af37d71
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Enrique Herrera-Viedma
- Francisco Chiclana
- Zeshui Xu
- Francisco Herrera
- Jian Wu
- Hamido Fujita
- Huchang Liao
- Xunjie Gou
In Management Science and Operations Research
- Enrique Herrera-Viedma
- Zeshui Xu
- Francisco Chiclana
- Francisco Herrera
- Huchang Liao
- Jian Wu
- Hamido Fujita
- Luis Martínez-López
Citations in the corpus
- Consensus Building for the Heterogeneous Large-Scale GDM With the Individual Concerns and Satisfactions (2018, neutral): “Based on the use of a soft consensus measure, a large number of CRPs have been reported, and they can be mainly classified in to the following types: 1) CRPs under different preference representation structures [5], [8], [16], [27], [31], [37], [50], [54], [56]; 2) CRPs with minimum adjustments or c”
- Double hierarchy hesitant fuzzy linguistic term set and MULTIMOORA method: A case of study to evaluate the implementation status of haze controlling measures (2017, background): “In the future, some research directions concerning the DHH-FLTSs can be developed including the double hierarchy hesitant fuzzy linguistic information aggregation, linguistic measures, preference relations and consistency analysis, personalized individual semantics [44] , etc.”
- A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust (2018, background): “Considering that in social network, more often than not, users express their preferences using natural language, it could be more realistic and practice in GDM to assume that linguistic information is used by users to express their trust preferences [26, 28, 30, 39, 53].”