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dc.contributor.authordel Moral, Maria Joseen
dc.contributor.authorTapia Garcia, Juan Miguelen
dc.contributor.authorChiclana, Franciscoen
dc.contributor.authorHerrera-Viedma, Enriqueen
dc.date.accessioned2018-10-23T08:10:58Z
dc.date.available2018-10-23T08:10:58Z
dc.date.issued2018-09
dc.identifier.citationDel Moral, M.J., Tapia Garcia, J.M., Chiclana, F., Herrera-Viedma, E. (2018) Comparing Two Approaches for Consensus Computation in Group Decision Making Problems. In: Fujita, H., Herrera-Viedma, E. (Eds.) New Trends in Intelligent Software Methodologies, Tools and Techniques. Frontiers in Artificial Intelligence and Applications, 303, pp.312 - 320.en
dc.identifier.isbn9781614998990
dc.identifier.urihttp://hdl.handle.net/2086/16787
dc.description.abstractIn group decision making problems, the soft consensus calculus is an important topic. Soft consensus measures are utilized to show the different agreement degrees between decisors. Using the concept of coincidence we have two main approaches to calculating soft consensus measures: concordance among expert preferences and concordance among individual solutions. In the first, the coincidence is obtained by evaluating the similarity among the expert preferences, while in the second one the concordance is derived from the measurement of the similarity among the solutions proposed by these decisors or experts. In this paper we perform a basic comparative study of consensus calculus based on both coincidence approaches. We use the nonparametric Wilcoxon signed-ranks test to compare these approaches. We obtain significant differences between both approaches for measuring consensus.en
dc.language.isoenen
dc.publisherIOS pressen
dc.subjectGroup decision makingen
dc.subjectfuzzy preference relationsen
dc.subjectconsensusen
dc.subjectWilcoxon testen
dc.titleComparing Two Approaches for Consensus Computation in Group Decision Making Problemsen
dc.typeBook chapteren
dc.identifier.doihttps://doi.org/10.3233/978-1-61499-900-3-312
dc.researchgroupInstitute of Artificial Intelligence (IAI)en
dc.peerreviewedYesen
dc.funderThe authors would like to acknowledge the FEDER financial support from the project TIN2016-75850-R.en
dc.projectidThe authors would like to acknowledge the FEDER financial support from the project TIN2016-75850-R.en
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2018-09-25en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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