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dc.contributor.authorChiclana, Franciscoen
dc.contributor.authorUrena, Raquelen
dc.contributor.authorHerrera-Viedma, Enriqueen
dc.date.accessioned2016-04-08T10:12:18Z
dc.date.available2016-04-08T10:12:18Z
dc.date.issued2016
dc.identifier.citationChiclana, F., Ureña, R. and Herrera-Viedma, E. (2016) Choice degrees in decision-making: A comparison between intuitionistic and fuzzy preference relations approaches. Accepted for presentation at FUZZ-IEEE2006 (WCCI2016).en
dc.identifier.urihttp://hdl.handle.net/2086/11848
dc.description.abstractPreference modelling based on Atanassov’s intuitionistic fuzzy sets are gaining increasing relevance in the field of group decision making as they provide experts with a flexible and simple tool to express their preferences on a set of alternative options, while allowing, at the same time, to accommodate experts’ preference uncertainty, which is inherent to all decision making processes. A key issue within this framework is the provision of efficient methods to rank alternatives, from best to worse, taking into account the peculiarities that this type of preference representation format presents. In this contribution we analyse the relationships between the main method proposed and used by researchers to rank alternatives using intuitionistic fuzzy sets, the score degree function, and the well known choice degree based on Orlovsky’s non-dominance concept for the case when the preferences are expressed by means of fuzzy preference relations. This relationship study will provide the necessary theoretical results to support the implementation of Orlovsky’s non-dominance concept to define the fuzzy quantifier guided non-dominance choice degree for intuitionistic fuzzy preference relations.en
dc.language.isoenen
dc.publisherIEEEen
dc.titleChoice degrees in decision-making: A comparison between intuitionistic and fuzzy preference relations approachesen
dc.typeConferenceen
dc.identifier.doihttps://doi.org/10.1109/fuzz-ieee.2016.7737916
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderThe authors would like to acknowledge the support from FEDER funds in the FUZZYLING-II Project TIN2010-17876, as well as the support from the Andalusian Excellence Projects TIC-05299 and TIC-5991.en
dc.projectidTIN2010-17876, TIC-05299 and TIC-5991en
dc.cclicenceCC-BY-NCen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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