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dc.contributor.authordel Moral, Maria Joseen
dc.contributor.authorChiclana, Franciscoen
dc.contributor.authorTapia, Juan Miguelen
dc.contributor.authorHerrera-Viedma, Enriqueen
dc.date.accessioned2017-03-02T13:42:22Z
dc.date.available2017-03-02T13:42:22Z
dc.date.issued2017
dc.identifier.citationDel Moral, M.J., Chiclana, F., Tapia, J.M. and Herrera-Viedma, E. (2017) An Analysis on Consensus Measures in Group Decision Making. 2017 4th International Conference on Control, Decision and Information Technologies (CoDIT'17), April 5-7, 2017 at Barcelona, Spain.en
dc.identifier.urihttp://hdl.handle.net/2086/13368
dc.description.abstractIn Group Decision Making (GDM) problems before to obtain a solution a high level of consensus among experts is required. Consensus measures are usually built using similarity functions measuring how close experts’ opinions or preferences are. Similarity functions are defined based on the use of a metric describing the distance between experts’ opinions or preferences. Different distance functions have been proposed to implement consensus measures. This paper analyzes the effect of the application of different aggregation operators combined with the use of different distance functions for measuring consensus in GDM problems. It is concluded that the application of different aggregation operators together with different distance functions has a significant effect on the speed of achieving consensus. These results are analysed and used to derive decision support rules, based on a convergent criterion, that can be used to control the convergence speed of the consensus process using the compared distance functions.en
dc.language.isoenen
dc.titleAn Analysis on Consensus Measures in Group Decision Makingen
dc.typeConferenceen
dc.identifier.doihttps://doi.org/10.1109/codit.2017.8102605
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderThe authors would like to acknowledge FEDER financial support from the project TIN2013-40658-P and the Andalusian Excellence Project TIC-05991 and also the research project MTM2012-35591.en
dc.projectidThe authors would like to acknowledge FEDER financial support from the project TIN2013-40658-P and the Andalusian Excellence Project TIC-05991 and also the research project MTM2012-35591.en
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2017-02-14en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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