Mostrar el registro sencillo del ítem

Artículo

dc.creatorÁlvarez de la Concepción, Miguel Ángeles
dc.creatorGonzález Abril, Luises
dc.creatorSoria Morillo, Luis Migueles
dc.creatorOrtega Ramírez, Juan Antonioes
dc.date.accessioned2017-03-01T11:35:40Z
dc.date.available2017-03-01T11:35:40Z
dc.date.issued2013
dc.identifier.citationÁlvarez de la Concepción, M.Á., González Abril, L., Soria Morillo, L.M. y Ortega Ramírez, J.A. (2013). An adaptive methodology to discretize and select features. Artificial Intelligence Research, 2 (2), 77-86.
dc.identifier.issn1927-6974es
dc.identifier.urihttp://hdl.handle.net/11441/55021
dc.description.abstractA lot of significant data describing the behavior or/and actions of systems can be collected in several domains. These data define some aspects, called features, that can be clustered in several classes. A qualitative or quantitative value for each feature is stored from measurements or observations. In this paper, the problem of finding independent features for getting the best accuracy on classification problems is considered. Obtaining these features is the main objective of this work, where an automatic method to select features is proposed. The method extends the functionality of Ameva coefficient to use it in other tasks of machine learning where it has not been defined.es
dc.description.sponsorshipMinisterio de Ciencia e Innovación ARTEMISA TIN2009-14378-C02-01es
dc.description.sponsorshipJunta de Andalucia Simon TIC-8052es
dc.formatapplication/pdfes
dc.language.isoenges
dc.publisherSciEdu Presses
dc.relation.ispartofArtificial Intelligence Research, 2 (2), 77-86.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAmevaes
dc.subjectFeature selectiones
dc.subjectDiscretizationes
dc.subjectEntropyes
dc.titleAn adaptive methodology to discretize and select featureses
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 Lenguajes y Sistemas Informáticoses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Economía Aplicada Ies
dc.relation.projectIDARTEMISA TIN2009-14378-C02-01es
dc.relation.projectIDSimon TIC-8052es
dc.relation.publisherversionhttp://www.sciedu.ca/journal/index.php/air/article/view/1331es
dc.identifier.doi10.5430/air.v2n2p77es
idus.format.extent10es
dc.journaltitleArtificial Intelligence Researches
dc.publication.volumen2es
dc.publication.issue2es
dc.publication.initialPage77es
dc.publication.endPage86es
dc.contributor.funderMinisterio de Ciencia e Innovación (MICIN). España
dc.contributor.funderJunta de Andalucía

FicherosTamañoFormatoVerDescripción
An_adaptive_methodology_to_dis ...101.1KbIcon   [PDF] Ver/Abrir  

Este registro aparece en las siguientes colecciones

Mostrar el registro sencillo del ítem

Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Excepto si se señala otra cosa, la licencia del ítem se describe como: Attribution-NonCommercial-NoDerivatives 4.0 Internacional