A preliminary investigation of overfitting in evolutionary driven model induction : implications for financial modelling

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dc.contributor.author Tuite, Cliodhna
dc.contributor.author Agapitos, Alexandros
dc.contributor.author O'Neill, Michael
dc.contributor.author Brabazon, Anthony
dc.date.accessioned 2011-08-02T16:28:31Z
dc.date.available 2011-08-02T16:28:31Z
dc.date.copyright Springer-Verlag Berlin Heidelberg 2011 en
dc.date.issued 2011-04
dc.identifier.isbn 978-3-642-20519-4
dc.identifier.uri http://hdl.handle.net/10197/3059
dc.description EvoStar 2011, 27-29 April, 2011, Torino Italy en
dc.description.abstract This paper investigates the effects of early stopping as a method to counteract overfitting in evolutionary data modelling using Genetic Programming. Early stopping has been proposed as a method to avoid model overtraining, which has been shown to lead to a significant degradation of out-of-sample performance. If we assume some sort of performance metric maximisation, the most widely used early training stopping criterion is the moment within the learning process that an unbiased estimate of the performance of the model begins to decrease after a strictly monotonic increase through the earlier learning iterations. We are conducting an initial investigation on the effects of early stopping in the performance of Genetic Programming in symbolic regression and financial modelling. Empirical results suggest that early stopping using the above criterion increases the extrapolation abilities of symbolic regression models, but is by no means the optimal training-stopping criterion in the case of a real-world financial dataset. en
dc.description.sponsorship Science Foundation Ireland en
dc.format.extent 654963 bytes
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Springer en
dc.relation.ispartof Di Chio, C. et al (eds.). Applications of Evolutionary Computation EvoApplications 2011: EvoCOMNET, EvoFIN, EvoHOT, EvoMUSART, EvoSTIM, and EvoTRANSLOG, Torino, Italy, April 27-29, 2011, Proceedings, Part II en
dc.relation.requires Business Research Collection en
dc.relation.requires CASL Research Collection en
dc.relation.requires FMC² Research Collection en
dc.subject Overfitting en
dc.subject Evolutionary data modelling en
dc.subject Genetic programming en
dc.subject.lcsh Evolutionary computation en
dc.subject.lcsh Genetic programming (Computer science) en
dc.subject.lcsh Finance--Computer simulation en
dc.title A preliminary investigation of overfitting in evolutionary driven model induction : implications for financial modelling en
dc.type Conference Publication en
dc.internal.availability Full text available en
dc.internal.webversions http://dx.doi.org/10.1007/978-3-642-20520-0_13
dc.status Peer reviewed en
dc.identifier.doi 10.1007/978-3-642-20520-0_13
dc.neeo.contributor Tuite|Cliodhna|aut|
dc.neeo.contributor Agapitos|Alexandros|aut|
dc.neeo.contributor O'Neill|Michael|aut|
dc.neeo.contributor Brabazon|Anthony|aut|
dc.description.admin 12M embargo: release in April 2012 - AV 2/08/2011 en

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