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dc.contributor.authorCalvo, B.
dc.contributor.authorCeberio, J.
dc.contributor.authorLozano, J.A.
dc.date.accessioned2018-08-31T12:17:55Z
dc.date.available2018-08-31T12:17:55Z
dc.date.issued2018-08-30
dc.identifier.isbn978-145035764-7
dc.identifier.urihttp://hdl.handle.net/20.500.11824/849
dc.description.abstractThe statistical assessment of the empirical comparison of algorithms is an essential step in heuristic optimization. Classically, researchers have relied on the use of statistical tests. However, recently, concerns about their use have arisen and, in many fields, other (Bayesian) alternatives are being considered. For a proper analysis, different aspects should be considered. In this work we focus on the question: what is the probability of a given algorithm being the best? To tackle this question, we propose a Bayesian analysis based on the Plackett-Luce model over rankings that allows several algorithms to be considered at the same timeen_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.titleBayesian inference for algorithm ranking analysisen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.identifier.doi10.1145/3205651.3205658
dc.relation.publisherversionhttps://dl.acm.org/citation.cfm?doid=3205651.3205658en_US
dc.relation.projectIDES/1PE/SEV-2013-0323en_US
dc.relation.projectIDEUS/BERC/BERC.2014-2017en_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.titleGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion 6 July 2018, Pages 324-325en_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España