Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/54648
Campo DC Valoridioma
dc.contributor.authorMartínez-García, José Manuelen_US
dc.contributor.authorSuárez-Araujo, Carmen Pazen_US
dc.contributor.authorBáez, Patricio Garcíaen_US
dc.date.accessioned2019-02-18T12:13:40Z-
dc.date.available2019-02-18T12:13:40Z-
dc.date.issued2012en_US
dc.identifier.isbn9783642344770en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/10553/54648-
dc.description.abstractIn this work we introduce a novel over-sampling method to face the problem of imbalanced classes’ classification. This method, based on the Sanger neural network, is capable of dealing with high-dimensional datasets. Moreover, it extends the capability of over-sampling methods and allows generating samples from both minority and majority classes. We have validated it in real medical applications where the involved datasets present an un-even representation among the classes and it has been obtained high sensitivities identifying minority classes. Therefore, by means of this method it is possible to accomplish the design of systems for the medical diagnosis with a high reliability.en_US
dc.languageengen_US
dc.publisher0302-9743en_US
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.sourceHuang T., Zeng Z., Li C., Leung C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7666. Springer, Berlin, Heidelbergen_US
dc.subject120304 Inteligencia artificialen_US
dc.subject32 Ciencias médicasen_US
dc.subject.otherNeural-Networks
dc.titleSNEOM: a sanger network based extended over-sampling method. Application to imbalanced biomedical datasetsen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.typeConferenceObjecten_US
dc.relation.conference19th International Conference on Neural Information Processing (ICONIP)
dc.relation.conference19th International Conference on Neural Information Processing, ICONIP 2012
dc.identifier.doi10.1007/978-3-642-34478-7_71
dc.identifier.scopus84869026976-
dc.identifier.isi000345091300071
dc.contributor.authorscopusid55479947300-
dc.contributor.authorscopusid6603605708-
dc.contributor.authorscopusid23476362100-
dc.description.lastpage592-
dc.description.firstpage584-
dc.relation.volume7666 LNCS-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Actas de congresosen_US
dc.contributor.daisngid25018362
dc.contributor.daisngid9879072
dc.contributor.daisngid2362390
dc.identifier.eisbn978-3-642-34478-7-
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Martinez-Garcia, JM
dc.contributor.wosstandardWOS:Suarez-Araujo, CP
dc.contributor.wosstandardWOS:Baez, PG
dc.date.coverdateNoviembre 2012
dc.identifier.conferenceidevents120879
dc.identifier.conferenceidevents121453
dc.identifier.ulpgces
dc.description.sjr0,323
dc.description.sjrqQ3
dc.description.ggs3
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.author.deptGIR IUCES: Computación inteligente, percepción y big data-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0002-8826-0899-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.fullNameSuárez Araujo, Carmen Paz-
crisitem.event.eventsstartdate11-11-2012-
crisitem.event.eventsstartdate11-11-2012-
crisitem.event.eventsenddate15-11-2012-
crisitem.event.eventsenddate15-11-2012-
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