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Predicting Problem Difficulty for Genetic Programming Applied to Data Classification

hal.structure.identifierInstituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana]
dc.contributor.authorTRUJILLO, Leonardo
hal.structure.identifierInstituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana]
dc.contributor.authorMARTINEZ, Yuliana
hal.structure.identifierSchool of Computer Science and Electronic Engineering
dc.contributor.authorGALVAN-LOPEZ, Edgar
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierAdvanced Learning Evolutionary Algorithms [ALEA]
dc.contributor.authorLEGRAND, Pierrick
dc.contributor.editorNatalio KrasnogorUniversity of Nottingham, UK
dc.date.issued2011
dc.date.conference2011-07-12
dc.description.abstractEnDuring the development of applied systems, an important problem that must be addressed is that of choosing the correct tools for a given domain or scenario. This general task has been addressed by the genetic programming (GP) community by attempting to determine the intrinsic difficulty that a problem poses for a GP search. This paper presents an approach to predict the performance of GP applied to data classification, one of themost common problems in computer science. The novelty of the proposal is to extract statistical descriptors and complexity descriptors of the problem data, and from these estimate the expected performance of a GP classifier. We derive two types of predictive models: linear regression models and symbolic regression models evolved with GP. The experimental results show that both approaches provide good estimates of classifier performance, using synthetic and real-world problems for validation. In conclusion, this paper shows that it is possible to accurately predict the expected performance of a GP classifier using a set of descriptors that characterize the problem data.
dc.language.isoen
dc.publisherACM New York, NY, USA ©2011
dc.title.enPredicting Problem Difficulty for Genetic Programming Applied to Data Classification
dc.typeCommunication dans un congrès
dc.identifier.doi10.1145/2001576.2001759
dc.subject.halInformatique [cs]/Traitement du signal et de l'image
dc.subject.halSciences de l'ingénieur [physics]/Traitement du signal et de l'image
dc.subject.halInformatique [cs]/Intelligence artificielle [cs.AI]
dc.subject.halMathématiques [math]/Statistiques [math.ST]
dc.subject.halStatistiques [stat]/Théorie [stat.TH]
bordeaux.page1355-1362
bordeaux.conference.titleGecco 2011
bordeaux.countryIE
bordeaux.conference.cityDublin
bordeaux.peerReviewedoui
hal.identifierhal-00643358
hal.version1
hal.invitednon
hal.proceedingsoui
hal.conference.end2011-07-16
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00643358v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2011&rft.spage=1355-1362&rft.epage=1355-1362&rft.au=TRUJILLO,%20Leonardo&MARTINEZ,%20Yuliana&GALVAN-LOPEZ,%20Edgar&LEGRAND,%20Pierrick&rft.genre=unknown


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