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RANSAC-GP: Dealing with Outliers in Symbolic Regression with Genetic Programming

hal.structure.identifierInstituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana]
dc.contributor.authorLOPEZ, Uriel
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.identifierUniversité de Bordeaux [UB]
hal.structure.identifierQuality control and dynamic reliability [CQFD]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorLEGRAND, Pierrick
hal.structure.identifierLaboratorio Nacional de Geointeligencia [GeoINT]
dc.contributor.authorNAREDO, Enrique
dc.contributor.authorSILVA, Sara
dc.date.accessioned2024-04-04T03:04:36Z
dc.date.available2024-04-04T03:04:36Z
dc.date.issued2017-03-15
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/193168
dc.description.abstractEnGenetic programming (GP) has been shown to be a powerful tool for automatic modeling and program induction. It is often used to solve difficult symbolic regression tasks, with many examples in real-world domains. However, the robustness of GP-based approaches has not been substantially studied. In particular, the present work deals with the issue of outliers, data in the training set that represent severe errors in the measuring process. In general, a datum is considered an outlier when it sharply deviates from the true behavior of the system of interest. GP practitioners know that such data points usually bias the search and produce inaccurate models. Therefore, this work presents a hybrid methodology based on the RAndom SAmpling Consensus (RANSAC) algorithm and GP, which we call RANSAC-GP. RANSAC is an approach to deal with outliers in parameter estimation problems, widely used in computer vision and related fields. On the other hand, this work presents the first application of RANSAC to symbolic regression with GP, with impressive results. The proposed algorithm is able to deal with extreme amounts of contamination in the training set, evolving highly accurate models even when the amount of outliers reaches 90%.
dc.language.isoen
dc.publisherSpringer
dc.source.titleGenetic Programming. EuroGP 2017. Lecture Notes in Computer Science, vol 10196. Springer, Cham
dc.title.enRANSAC-GP: Dealing with Outliers in Symbolic Regression with Genetic Programming
dc.typeChapitre d'ouvrage
dc.subject.halInformatique [cs]/Intelligence artificielle [cs.AI]
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.title.proceedingGenetic Programming. EuroGP 2017. Lecture Notes in Computer Science, vol 10196. Springer, Cham
hal.identifierhal-01911448
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01911448v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=Genetic%20Programming.%20EuroGP%202017.%20Lecture%20Notes%20in%20Computer%20Science,%20vol%2010196.%20Springer,%20Cham&rft.date=2017-03-15&rft.au=LOPEZ,%20Uriel&TRUJILLO,%20Leonardo&MARTINEZ,%20Yuliana&LEGRAND,%20Pierrick&NAREDO,%20Enrique&rft.genre=unknown


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