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    Dealing with Incomplete Information in Linguistic Group Decision Making by Means of Interval Type-2 Fuzzy Sets

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    Author's copy of accepted paper. (473.9Kb)
    Date
    2019-01-29
    Author
    Urena, Raquel;
    Kou, Gang;
    Wu, Jian;
    Chiclana, Francisco;
    Herrera-Viedma, Enrique
    Metadata
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    Abstract
    Nowadays in the social network based decision making processes, as the ones involved in e-commerce and e-democracy, multiple users with di erent backgrounds may take part and diverse alternatives might be involved. This diversity enriches the process but at the same time increases the uncertainty in the opinions. This uncertainty can be considered from two di erent perspectives: (i) the uncertainty in the meaning of the words given as preferences, that is motivated by the heterogeneity of the decision makers, (ii) the uncertainty inherent to any decision making process that may lead to an expert not being able to provide all their judgments. The main objective of this contribution is to address these two type of uncertainty. To do so the following approaches are proposed: Firstly, in order to capture, process and keep the uncertainty in the meaning of the linguistic assumption the Interval Type 2 Fuzzy Sets are introduced as a way to model the experts linguistic judgments. Secondly, a measure of the coherence of the information provided by each decision maker is proposed. Finally, a consistency based completion approach is introduced to deal with the uncertainty presented in the expert judgments. The proposed approach is tested in an e-democracy decision making scenario.
    Description
    The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
    Citation : Ureña, R., Kou, G., Wu, J., Chiclana, F., Herrera-Viedma, E. (2019) Dealing with Incomplete Information in Linguistic Group Decision Making by Means of Interval Type 2 Fuzzy Sets. International Journal of Intelligent Systems,
    URI
    http://hdl.handle.net/2086/17489
    DOI
    https://doi.org/10.1002/int.22095
    ISSN : 0884-8173
    Research Group : Institute of Artificial Intelligence (IAI)
    Research Institute : Institute of Artificial Intelligence (IAI)
    Peer Reviewed : Yes
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    • School of Computer Science and Informatics [2966]

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