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Strong Typing, Variable Reduction and Bloat Control for Solving the Bankruptcy Prediction Problem Using Genetic Programming

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Book cover Natural Computing in Computational Finance

Part of the book series: Studies in Computational Intelligence ((SCI,volume 100))

Summary

In this chapter we present the application of a genetic programming (GP) algorithm to the problem of bankruptcy prediction. To carry out the research we have used a database that includes extensive information (not only economic) from the companies. In order to handle the different data types we have used Strongly Typed GP and variable reduction. Also, bloat control has been implemented to obtain comprehensible classification models. For comparison purposes we have solved the same problem using a support vector machine (SVM). GP has achieved very satisfactory results, improving those obtained with the SVM.

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Authors and Affiliations

  1. Instituto Tecnológico de Informática, Universidad Politécnica de Valencia, Valencia, Spain

    Eva Alfaro-Cid, Alberto Cuesta-Cañada, Ken Sharman & Anna I. Esparcia-Alcázar

Authors
  1. Eva Alfaro-Cid
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  2. Alberto Cuesta-Cañada
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  3. Ken Sharman
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  4. Anna I. Esparcia-Alcázar
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Editor information

Editors and Affiliations

  1. School of Business Quinn School, University College Dublin, Belfield, Dublin 4, Ireland

    Anthony Brabazon (Head of Research) (Head of Research)

  2. Natural Computing Research and Applications School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland

    Michael O’Neill (Director) (Director)

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© 2008 Springer-Verlag Berlin Heidelberg

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Alfaro-Cid, E., Cuesta-Cañada, A., Sharman, K., Esparcia-Alcázar, A.I. (2008). Strong Typing, Variable Reduction and Bloat Control for Solving the Bankruptcy Prediction Problem Using Genetic Programming. In: Brabazon, A., O’Neill, M. (eds) Natural Computing in Computational Finance. Studies in Computational Intelligence, vol 100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77477-8_9

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  • DOI: https://doi.org/10.1007/978-3-540-77477-8_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77476-1

  • Online ISBN: 978-3-540-77477-8

  • eBook Packages: EngineeringEngineering (R0)

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