Generating Recommendations in GDM with an Allocation of Information Granularity
Cabrerizo, Francisco Javier
Morente-Molinera, Juan Antonio (UNIR)
Perez, Javier Ignacio
Tipo de Ítem:Articulo Revista Indexada
A Group decision making process is carried out when human beings jointly make an election from a possible collection of alternatives. Here, a question of importance is to avoid winners and losers, in the sense that the choice is not any more attributable to any single individual, but all group members contribute to the decision. For this reason, the agreement or consensus achieved among all the individuals should be as high as possible. In this contribution, a feedback mechanism is presented in order to increase the consensus achieved among the decision makers involved in this kind of problems. It is based on granular computing, which is utilized here to provide the necessary flexibility to increase the consensus. The feedback mechanism is able to deal with heterogeneous contexts, that is, contexts in which the decision makers have importance degrees considering their capacity or talent to handle the problem.
Este ítem aparece en la(s) siguiente(s) colección(es)
Mostrando ítems relacionados por Título, autor o materia.
Cabrerizo, Francisco Javier; Morente-Molinera, Juan Antonio (UNIR); Pedrycz, Witold; Taghavi, Atefe; Herrera-Viedma, Enrique (Expert Systems with Applications, 01/07/2018)This study is concerned with group decision making contexts in which linguistic preference relations are used to provide the evaluations of results. On the one hand, granulation of linguistic terms, which are used as entries ...
Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods Morente-Molinera, Juan Antonio (UNIR); Kou, G; González-Crespo, Rubén (UNIR); Corchado, J M; Herrera-Viedma, Enrique (Knowledge-Based Systems, 12/2017)Classic multi-criteria group decision making models that have a high amount of alternatives are unmanageable for the experts. This is because they have to provide one value per each alternative and criteria. In this paper, ...
Improving supervised learning classification methods using multi-granular linguistic modelling and fuzzy entropy Morente-Molinera, Juan Antonio (UNIR); Mezei, Jozsef; Carlsson, Christer; Herrera-Viedma, Enrique (IEEE Transactions on Fuzzy Systems, 10/2017)Obtaining good classification results using supervised learning methods is critical if we want to obtain a high level of precision in the classification processes. The training data used for the learning process plays a ...