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Application of Genetic Programming to Induction of Linear Classification Trees

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Genetic Programming (EuroGP 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1802))

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Abstract

A common problem in datamining is to find accurate classifiers for a dataset. For this purpose, genetic programming (GP) is applied to a set of benchmark classification problems. Using GP we are able to induce decision trees with a linear combination of variables in each function node. A new representation of decision trees using strong typing in GP is introduced. With this representation it is possible to let the GP classify into any number of classes. Results indicate that GP can be applied successfully to classification problems. Comparisons with current state-of-the-art algorithms in machine learning are presented and areas of future research are identified.

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Author information

Authors and Affiliations

  1. Vrije Universiteit, De Boelelaan 1081, 1081 HV, Amsterdam

    Martijn C. J. Bot

  2. CWI, Kruislaan 413, 1098 SJ, Amsterdam

    William B. Langdon

Authors
  1. Martijn C. J. Bot
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  2. William B. Langdon
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Editor information

Editors and Affiliations

  1. Department of Computing and Electronic Systems, University of Essex, Wivenhoe Park, CO4 3SQ, Colchester, UK

    Riccardo Poli

  2. Department of Computer Science, Memorial University of Newfoundland, Canada

    Wolfgang Banzhaf

  3. Departments of Mathematical, Biological Sciences and Computing and Electronic Systems, University of Essex, UK

    William B. Langdon

  4. School of Computer Science, The University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK

    Julian Miller

  5. Dept. of Physical Rescource Theory, Chalmers University of Technology, 412 96, Göteborg, Sweden

    Peter Nordin

  6. School of Computing, Information Systems and Mathematics, South Bank University, SE1 0AA, London, UK

    Terence C. Fogarty

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© 2000 Springer-Verlag Berlin Heidelberg

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Bot, M.C.J., Langdon, W.B. (2000). Application of Genetic Programming to Induction of Linear Classification Trees. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds) Genetic Programming. EuroGP 2000. Lecture Notes in Computer Science, vol 1802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-46239-2_18

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  • DOI: https://doi.org/10.1007/978-3-540-46239-2_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67339-2

  • Online ISBN: 978-3-540-46239-2

  • eBook Packages: Springer Book Archive

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