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dc.contributor.editorAguayo-González, Franciscoes
dc.contributor.editorLeón de Mora, Carloses
dc.creatorLuque Sendra, Amaliaes
dc.creatorCarrasco Muñoz, Alejandroes
dc.creatorMartín-Gómez, Alejandro Manueles
dc.creatorLama-Ruiz, Juan Ramónes
dc.date.accessioned2019-02-06T10:48:37Z
dc.date.available2019-02-06T10:48:37Z
dc.date.issued2019
dc.identifier.citationLuque Sendra, A., Carrasco Muñoz, A., Martín Gómez, A.M. y Lama-Ruiz, J.R. (2019). Exploring Symmetry of Binary Classification Performance Metrics. Symmetry, 11 (1), 47-.
dc.identifier.issn2073-8994es
dc.identifier.issn2073-8994es
dc.identifier.urihttps://hdl.handle.net/11441/82577
dc.description.abstractSelecting the proper performance metric constitutes a key issue for most classification problems in the field of machine learning. Although the specialized literature has addressed several topics regarding these metrics, their symmetries have yet to be systematically studied. This research focuses on ten metrics based on a binary confusion matrix and their symmetric behaviour is formally defined under all types of transformations. Through simulated experiments, which cover the full range of datasets and classification results, the symmetric behaviour of these metrics is explored by exposing them to hundreds of simple or combined symmetric transformations. Cross-symmetries among the metrics and statistical symmetries are also explored. The results obtained show that, in all cases, three and only three types of symmetries arise: labelling inversion (between positive and negative classes); scoring inversion (concerning good and bad classifiers); and the combination of these two inversions. Additionally, certain metrics have been shown to be independent of the imbalance in the dataset and two cross-symmetries have been identified. The results regarding their symmetries reveal a deeper insight into the behaviour of various performance metrics and offer an indicator to properly interpret their values and a guide for their selection for certain specific applications.es
dc.description.sponsorshipUniversity of Seville (Spain) by Telefónica Chair “Intelligence in Networks”es
dc.formatapplication/pdfes
dc.language.isoenges
dc.publisherMDPIes
dc.relation.ispartofSymmetry, 11 (1), 47-.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectPerformance metricses
dc.subjectClassificationes
dc.subjectComputational symmetryes
dc.subjectMachine learninges
dc.titleExploring Symmetry of Binary Classification Performance Metricses
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Ingeniería del Diseñoes
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Tecnología Electrónicaes
dc.relation.publisherversionwww.mdpi.com/2073-8994/11/1/47es
dc.identifier.doi10.3390/sym11010047es
dc.contributor.groupUniversidad de Sevilla. TEP022: Diseño Industrial e Ingeniería del Proyecto y la Innovaciónes
dc.contributor.groupUniversidad de Sevilla. TIC150: Tecnología Electrónica e Informática Industriales
idus.format.extent31 p.es
dc.journaltitleSymmetryes
dc.publication.volumen11es
dc.publication.issue1es
dc.publication.initialPage47es
dc.contributor.funderUniversidad de Sevilla

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