Tackling overfitting in evolutionary-driven financial model induction

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Title: Tackling overfitting in evolutionary-driven financial model induction
Authors: Tuite, Clíodhna
Agapitos, Alexandros
O'Neill, Michael
Brabazon, Anthony
Permanent link: http://hdl.handle.net/10197/3494
Date: 2011
Abstract: This chapter explores the issue of overfitting in grammar-based Genetic Programming. Tools such as Genetic Programming are well suited to problems in finance where we seek to learn or induce a model from data. Models that overfit the data upon which they are trained prevent model generalisation, which is an important goal of learning algorithms. Early stopping is a technique that is frequently used to counteract overfitting, but this technique often fails to identify the optimal point at which to stop training. In this chapter, we implement four classes of stopping criteria, which attempt to stop training when the generalisation of the evolved model is maximised. We show promising results using, in particular, one novel class of criteria, which measured the correlation between the training and validation fitness at each generation. These criteria determined whether or not to stop training depending on the measurement of this correlation - they had a high probability of being the best among a suite of potential criteria to be used during a run. This meant that they often found the lowest validation set error for the entire run faster than other criteria.
Funding Details: Science Foundation Ireland
Type of material: Book Chapter
Publisher: Springer
Copyright (published version): Springer-Verlag Berlin Heidelberg 2011
Keywords: Genetic programming;Over-fitting;Symbolic regression;Early stopping
Subject LCSH: Genetic programming (Computer science)
Finance--Mathematical models
Machine learning
DOI: 10.1007/978-3-642-23336-4_8
Language: en
Status of Item: Peer reviewed
Is part of: Brabazon, A., O'Neill, M., and Dietmar, M. (eds.). Natural Computing in Computational Finance (Volume IV)
Appears in Collections:FMC² Research Collection

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