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Prediction of Expected Performance for a Genetic Programming Classifier

hal.structure.identifierInstituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana]
dc.contributor.authorMARTINEZ, Yuliana
hal.structure.identifierInstituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana]
dc.contributor.authorTRUJILLO, Leonardo
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierQuality control and dynamic reliability [CQFD]
dc.contributor.authorLEGRAND, Pierrick
hal.structure.identifierTrinity College Dublin
dc.contributor.authorGALVAN-LOPEZ, Edgar
dc.date.accessioned2024-04-04T03:16:25Z
dc.date.available2024-04-04T03:16:25Z
dc.date.created2015
dc.date.issued2016
dc.identifier.issn1389-2576
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/194213
dc.description.abstractEnThe study of problem difficulty is an open issue in Genetic Programming (GP). Thegoal of this work is to generate models that predict the expected performance of a GPbasedclassifier when it is applied to an unseen task. Classification problems aredescribed using domain-specific features, some of which are proposed in this work,and these features are given as input to the predictive models. These models arereferred to as predictors of expected performance (PEPs). We extend this approach byusing an ensemble of specialized predictors (SPEP), dividing classification problemsinto specified groups and choosing the corresponding SPEP. The proposed predictorsare trained using 2D synthetic classification problems with balanced datasets. Themodels are then used to predict the performance of the GP classifier on unseen realworlddatasets that are multidimensional and imbalanced. Moreover, as we know, thiswork is the first to provide a performance prediction of the GP classifier on test data,while previous works focused on predicting training performance. Accurate predictivemodels are generated by posing a symbolic regression task and solving it with GP.These results are achieved by using highly descriptive features and including adimensionality reduction stage that simplifies the learning and testing process. Theproposed approach could be extended to other classification algorithms and used asthe basis of an expert system for algorithm selection.
dc.language.isoen
dc.publisherSpringer Verlag
dc.subject.enProblem Difficulty
dc.subject.enPrediction of Expected Performance
dc.subject.enSupervised Learning
dc.title.enPrediction of Expected Performance for a Genetic Programming Classifier
dc.typeArticle de revue
dc.identifier.doi10.1007/s10710-016-9265-9
dc.subject.halInformatique [cs]/Intelligence artificielle [cs.AI]
dc.subject.halStatistiques [stat]
dc.subject.halStatistiques [stat]/Machine Learning [stat.ML]
dc.description.sponsorshipEuropeAnalysis and classification of mental states of vigilance with evolutionary computation
bordeaux.journalGenetic Programming and Evolvable Machines
bordeaux.page409–449
bordeaux.volume17
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue4
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-01252141
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01252141v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Genetic%20Programming%20and%20Evolvable%20Machines&rft.date=2016&rft.volume=17&rft.issue=4&rft.spage=409%E2%80%93449&rft.epage=409%E2%80%93449&rft.eissn=1389-2576&rft.issn=1389-2576&rft.au=MARTINEZ,%20Yuliana&TRUJILLO,%20Leonardo&LEGRAND,%20Pierrick&GALVAN-LOPEZ,%20Edgar&rft.genre=article


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