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dc.creatorRuiz, Robertoes
dc.creatorPontes Balanza, Beatrizes
dc.creatorGiráldez, Raúles
dc.creatorAguilar Ruiz, Jesús Salvadores
dc.date.accessioned2022-03-01T11:00:28Z
dc.date.available2022-03-01T11:00:28Z
dc.date.issued2006
dc.identifier.citationRuiz, R., Pontes Balanza, B., Giráldez, R. y Aguilar Ruiz, J.S. (2006). Gene Ranking from Microarray Data for Cancer Classification : A Machine Learning Approach. En KES 2006: 10th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (1272-1280), Bournemouth, UK: Springer.
dc.identifier.isbn978-3-540-46537-9es
dc.identifier.issn0302-9743es
dc.identifier.urihttps://hdl.handle.net/11441/130281
dc.description.abstractTraditional gene selection methods often select the top–ranked genes according to their individual discriminative power. We propose to apply feature evaluation measure broadly used in the machine learning field and not so popular in the DNA microarray field. Besides, the application of sequential gene subset selection approaches is included. In our study, we propose some well-known criteria (filters and wrappers) to rank attributes, and a greedy search procedure combined with three subset evaluation measures. Two completely different machine learning classifiers are applied to perform the class prediction. The comparison is performed on two well–known DNA microarray data sets. We notice that most of the top-ranked genes appear in the list of relevant–informative genes detected by previous studies over these data sets.es
dc.description.sponsorshipComisión Interministerial de Ciencia y Tecnología (CICYT) TIN2004–00159es
dc.description.sponsorshipComisión Interministerial de Ciencia y Tecnología (CICYT) TIN2004-06689C0303es
dc.formatapplication/pdfes
dc.format.extent9es
dc.language.isoenges
dc.publisherSpringeres
dc.relation.ispartofKES 2006: 10th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (2006), pp. 1272-1280.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleGene Ranking from Microarray Data for Cancer Classification : A Machine Learning Approaches
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/submittedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticoses
dc.relation.projectIDTIN2004–00159es
dc.relation.projectIDTIN2004-06689C0303es
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/11893004_161es
dc.identifier.doi10.1007/11893004_161es
dc.publication.initialPage1272es
dc.publication.endPage1280es
dc.eventtitleKES 2006: 10th International Conference on Knowledge-Based and Intelligent Information and Engineering Systemses
dc.eventinstitutionBournemouth, UKes
dc.relation.publicationplaceBerlin, Germanyes
dc.identifier.sisius6533880es
dc.contributor.funderComisión Interministerial de Ciencia y Tecnología (CICYT). Españaes

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