An analysis of consensus approaches based on different concepts of coincidence

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dc.contributor.author del Moral, Maria Jose en
dc.contributor.author Tapia Garcia, Juan Miguel en
dc.contributor.author Chiclana, Francisco en
dc.contributor.author Al-Hmouz, A en
dc.contributor.author Herrera-Viedma, Enrique en
dc.date.accessioned 2017-12-20T11:06:46Z
dc.date.available 2017-12-20T11:06:46Z
dc.date.issued 2017-12
dc.identifier.citation del Moral, M. J., Tapia, J. M., Chiclana, F., Al-Hmouz, A., Herrera-Viedma, E (2017) An analysis of consensus approaches based on different concepts of coincidence. Journal of Intelligent and Fuzzy Systems . Accepted on 8th December 2017 en
dc.identifier.issn 1064-1246
dc.identifier.issn 1875-8967
dc.identifier.uri http://hdl.handle.net/2086/15034
dc.description The file attached to this record is the author's final peer reviewed version. en
dc.description.abstract Soft consensus is a relevant topic in group decision making problems. Soft consensus measures are utilized to reflect the different agreement degrees between the experts leading the consensus reaching process. This may determine the final decision and the time needed to reach it. The concept of coincidence has led to two main approaches to calculating the soft consensus measures, namely, concordance among expert preferences and concordance among individual solutions. In the first approach 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 experts. This paper performs a comparative study of consensus approaches based on both coincidence approaches. We obtain significant differences between both approaches by comparing several distance functions for measuring expert preferences and a consensus measure over the set of alternatives for measuring the solutions provided by experts. To do so, we use the nonparametric Wilcoxon signed-ranks test. Finally, these outcomes are analyzed using Friedman mean ranks in order to obtain a quantitative classification of the considered measurements according to the convergence criterion considered in the consensus reaching process. en
dc.language.iso en_US en
dc.publisher IOS Press en
dc.subject Group decision making en
dc.subject fuzzy preference relations en
dc.subject consensus en
dc.title An analysis of consensus approaches based on different concepts of coincidence en
dc.type Article en
dc.researchgroup Centre for Computational Intelligence en
dc.peerreviewed Yes en
dc.explorer.multimedia No en
dc.funder The authors would like to acknowledge the FEDER financial support from the project TIN2016-75850-R. en
dc.projectid TIN2016-75850-R en
dc.cclicence CC-BY-NC en
dc.date.acceptance 2017-12-08 en


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