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dc.contributor.authorQuiñonez, Brenda
dc.contributor.authorPinto Roa, Diego Pedro 
dc.contributor.authorGarcía Torres, Miguel
dc.contributor.authorGarcía-Diaz, María E.
dc.contributor.authorNúñez Castillo, Carlos Heriberto 
dc.contributor.authorDivina, Federico
dc.contributor.otherUniversidad Nacional de Asunción - Facultad Politécnicaes
dc.date.accessioned2022-04-27T23:42:14Z
dc.date.available2022-04-27T23:42:14Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/20.500.14066/3734
dc.description.abstractIn the High-dimensional data analysis there are several challenges in the fields of machine learning and data mining. Typically, feature selection is considered as a combinatorial optimization problem which seeks to remove irrelevant and redundant data by reducing computation time and improve learning measures. Given the complexity of this problem, we propose a novel Map-Elites based Algorithm that determines the minimum set of features maximizing learning accuracy simultaneously. Experimental results, on several data based from real scenarios, show the effectiveness of the proposed algorithm.es
dc.description.sponsorshipCONACYT – Consejo Nacional de Ciencia y Tecnologíaes
dc.language.isoenges
dc.subject.classification4 Transporte, telecomunicaciones y otras infraestructurases
dc.subject.otherFEATURE SELECTIONes
dc.subject.otherMAP-ELITESes
dc.subject.otherCOMBINATORIAL OPTIMIZATIONes
dc.subject.otherMACHINE LEARNINGes
dc.subject.otherDATA MININGes
dc.titleMap-elites algorithm for features selection problemes
dc.typeresearch articlees
dc.conference.date2019
dc.conference.placeAsunción, PYes
dc.conference.titleInternational Workshop on Foundation of Databases and the Web (AMW 2019)es
dc.description.fundingtextPROCIENCIAes
dc.page.initial1es
dc.page.final5es
dc.relation.projectCONACYTPINV15-257es
dc.rights.accessRightsopen accesses
dc.subject.ocdeINFORMATICAes


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