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

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Title: A preliminary investigation of overfitting in evolutionary driven model induction : implications for financial modelling
Authors: Tuite, Cliodhna
Agapitos, Alexandros
O'Neill, Michael
Brabazon, Anthony
Permanent link: http://hdl.handle.net/10197/3059
Date: Apr-2011
Online since: 2011-08-02T16:28:31Z
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.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Springer
Copyright (published version): Springer-Verlag Berlin Heidelberg 2011
Keywords: OverfittingEvolutionary data modellingGenetic programming
Subject LCSH: Evolutionary computation
Genetic programming (Computer science)
Finance--Computer simulation
DOI: 10.1007/978-3-642-20520-0_13
Other versions: http://dx.doi.org/10.1007/978-3-642-20520-0_13
Language: en
Status of Item: Peer reviewed
Is part of: 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
Conference Details: EvoStar 2011, 27-29 April, 2011, Torino Italy
ISBN: 978-3-642-20519-4
Appears in Collections:FMC² Research Collection
Business Research Collection
CASL Research Collection

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