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dc.contributor.authorElola, Andoni
dc.contributor.authorDel Ser, Javier
dc.contributor.authorBilbao, Miren Nekane
dc.contributor.authorPerfecto, Cristina
dc.contributor.authorAlexandre, Enrique
dc.contributor.authorSalcedo-Sanz, Sancho
dc.date.accessioned2016-11-28T15:28:02Z
dc.date.available2016-11-28T15:28:02Z
dc.date.issued2016
dc.identifier.citationAndoni Elola, Javier Del Ser, Miren Nekane Bilbao, Cristina Perfecto, Enrique Alexandre, Sancho Salcedo-Sanz, Hybridizing Cartesian Genetic Programming and Harmony Search for adaptive feature construction in supervised learning problems, Applied Soft Computing, Volume 52, March 2017, Pages 760-770, ISSN 1568-4946, http://dx.doi.org/10.1016/j.asoc.2016.09.049
dc.identifier.issn1568-4946en
dc.identifier.urihttp://hdl.handle.net/11556/347
dc.description.abstractThe advent of the so-called Big Data paradigm has motivated a flurry of research aimed at enhancing machine learning models by following very diverse approaches. In this context this work focuses on the automatic construction of features in supervised learning problems, which differs from the conventional selection of features in that new characteristics with enhanced predictive power are inferred from the original dataset. In particular this manuscript proposes a new iterative feature construction approach based on a self-learning meta-heuristic algorithm (Harmony Search) and a solution encoding strategy (correspondingly, Cartesian Genetic Programming) suited to represent combinations of features by means of constant-length solution vectors. The proposed feature construction algorithm, coined as Adaptive Cartesian Harmony Search (ACHS), incorporates modifications that allow exploiting the estimated predictive importance of intermediate solutions and, ultimately, attaining better convergence rate in its iterative learning procedure. The performance of the proposed ACHS scheme is assessed and compared to that rendered by the state of the art in a toy example and three practical use cases from the literature. The excellent performance figures obtained in these problems shed light on the widespread applicability of the proposed scheme to supervised learning with legacy datasets composed by already refined characteristics.en
dc.description.sponsorshipThis work has been funded in part by the Basque Government under the ELKARTEK program (BID3A project, grant ref. KK-2015/0000080)en
dc.language.isoengen
dc.publisherELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDSen
dc.titleHybridizing Cartesian Genetic Programming and Harmony Search for adaptive feature construction in supervised learning problemsen
dc.typearticleen
dc.identifier.doi10.1016/j.asoc.2016.09.049en
dc.isiYesen
dc.rights.accessRightsembargoedAccessen
dc.subject.keywordsFeature constructionen
dc.subject.keywordsSupervised learningen
dc.subject.keywordsCartesian Genetic Programmingen
dc.subject.keywordsHarmony Searchen
dc.identifier.essn1872-9681en
dc.journal.titleApplied Soft Computingen
dc.page.final770
dc.page.initial760
dc.volume.number52


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