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dc.contributor.authorShafti, Leila Shila
dc.contributor.authorPérez, Eduardo
dc.contributor.otherUAM. Departamento de Ingeniería Informáticaes_ES
dc.date.accessioned2015-03-06T13:02:44Z
dc.date.available2015-03-06T13:02:44Z
dc.date.issued2003
dc.identifier.citationAdvances in Intelligent Data Analysis V: 5th International Symposium on Intelligent Data Analysis, IDA 2003, Berlin, Germany, August 28-30, 2003. Proceedings. Lecture Notes in Computer Science, Volumen 2810. Springer, 2003. 599-610en_US
dc.identifier.isbn978-3-540-40813-0 (print)en_US
dc.identifier.isbn978-3-540-45231-7 (online)en_US
dc.identifier.issn0302-9743 (print)en_US
dc.identifier.issn1611-3349 (online)en_US
dc.identifier.urihttp://hdl.handle.net/10486/664367
dc.descriptionThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-45231-7_55en_US
dc.descriptionProceedings of 5th International Symposium on Intelligent Data Analysis, IDA 2003, Berlin, Germany, August 28-30, 2003en_US
dc.description.abstractThe aim of constructive induction (CI) is to transform the original data representation of hard concepts with complex interaction into one that outlines the relation among attributes. CI methods based on greedy search suffer from the local optima problem because of high variation in the search space of hard learning problems. To reduce the local optima problem, we propose a CI method based on genetic (evolutionary) algorithms. The method comprises two integrated genetic algorithms to construct functions over subsets of attributes in order to highlight regularities for the learner. Using non-algebraic representation for constructed functions assigns an equal degree of complexity to functions. This reduces the difficulty of constructing complex features. Experiments show that our method is comparable with and in some cases superior to existing CI methods.en_US
dc.description.sponsorshipThis work has been partially supported by the Spanish Interdepartmental Commission for Science and Technology (CICYT), under Grants numbers TIC98-0247-C02-02 and TIC2002-1948.en_US
dc.format.extent13 pág.es_ES
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherSpringer Berlin Heidelberg
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.rights© Springer-Verlag Berlin Heidelberg 2003
dc.subject.otherInformation Storage and Retrievalen_US
dc.subject.otherProbability and Statistics in Computer Scienceen_US
dc.subject.otherArtificial Intelligenceen_US
dc.subject.otherPattern Recognitionen_US
dc.subject.otherBusiness Information Systemsen_US
dc.subject.otherComputer Applications in Administrative Data Processingen_US
dc.titleGenetic approach to constructive induction based on non-algebraic feature representationen_US
dc.typeconferenceObjecten
dc.typebookParten
dc.subject.ecienciaInformáticaes_ES
dc.relation.publisherversionhttp://dx.doi.org/10.1007/978-3-540-45231-7_55
dc.identifier.doi10.1007/978-3-540-45231-7_55
dc.identifier.publicationfirstpage599
dc.identifier.publicationlastpage610
dc.identifier.publicationvolume2810
dc.relation.eventdateAugust 28-30, 2003en_US
dc.relation.eventnumber5
dc.relation.eventplaceBerlin (Germany)en_US
dc.relation.eventtitle5th International Symposium on Intelligent Data Analysis, IDA 2003en_US
dc.type.versioninfo:eu-repo/semantics/acceptedVersionen
dc.contributor.groupHerramientas Interactivas Avanzadas (ING EPS-003)es_ES
dc.rights.accessRightsopenAccessen
dc.authorUAMShafti , Leila Shila (261155)
dc.facultadUAMEscuela Politécnica Superior


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