Skip to main content
SpringerLink
Log in

Evolving Fuzzy Decision Trees with Genetic Programming and Clustering

  • Conference paper
  • First Online:
Genetic Programming (EuroGP 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2278))

Included in the following conference series:

Abstract

In this paper we present a new fuzzy decision tree representation for data classification using genetic programming. The new fuzzy representation utilizes fuzzy clusters for handling continuous attributes. To make optimal use of the fuzzy classifications of this representation an extra fitness measure is used. The new fuzzy representation will be compared, using several machine learning data sets, to a similar non-fuzzy representation as well as to some other evolutionary and non-evolutionary algorithms from literature.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. W. Banzhaf, P. Nordin, R.E. Keller, and F.D. Francone. Genetic Programming-An Introduction; On the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann, 1998.

    Google Scholar 

  2. L. Breiman. Bagging predictors. Machine Learning, 26(2):123–140, 1996.

    Google Scholar 

  3. J. Eggermont and J. I. van Hemert. Adaptive genetic programming applied to new and existing simple regression problems. In J. Miller, M. Tomassini, P.L. Lanzi, C. Ryan, A.G.B. Tetamanzi, and W.B. Langdon, editors, Proceedings on the Fourth European Conference on Genetic Programming (EuroGP’01), volume 2038 of LNCS, pages 23–35. Springer-Verlag, 2001.

    Google Scholar 

  4. Y. Freund and R.E. Schapire. Experiments with a new boosting algorithm. In Proc. 13th International Conference on Machine Learning, pages 148–146. Morgan Kaufmann, 1996.

    Google Scholar 

  5. J.M. de Graaf, W.A. Kosters, and J.J.W. Witteman. Interesting fuzzy association rules in quantitative databases. In L. de Raedt and A. Siebes, editors, 5th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD’01), volume 2168 of LNAI, pages 140–151. Springer Verlag, 2001.

    Google Scholar 

  6. Th.D. Haynes, D.A. Schoenefeld, and R. L. Wainwright. Type inheritance in strongly typed genetic programming. In P.J. Angeline and K.E. Kinnear, Jr., editors,Advances in Genetic Programming 2, chapter 18, pages 359–376. MIT Press, Cambridge, MA, USA, 1996.

    Google Scholar 

  7. L. Kaufman. Finding groups in data: An introduction to cluster analysis. In Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York, 1990.

    Google Scholar 

  8. M. Keijzer, J. J. Merelo, G. Romero, and M. Schoenauer. Evolving objects: A general purpose evolutionary computation library. In P. Collet et al., editor, Proceedings of Evolution Artificielle’01, LNCS. Springer Verlag, 2001. To appear.

    Google Scholar 

  9. J.R. Koza. Genetic Programming. MIT Press, 1992.

    Google Scholar 

  10. J.J. Liu and J.T. Kwok. An extended genetic rule induction algorithm. In Proc. of the 2000 Congress on Evolutionary Computation, pages 458–463, Piscataway, NJ,2000. IEEE Service Center.

    Google Scholar 

  11. R.R.F. Mendes, F.B. Voznika, A.A. Freitas, and J.C. Nievola. Discovering fuzzy classification rules with genetic programming and co-evolution. In L. de Raedt and A. Siebes, editors, 5th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD’01), volume 2168 of LNAI, pages 314–325.Springer Verlag, 2001.

    Google Scholar 

  12. D.J. Montana. Strongly typed genetic programming. Evolutionary Computation,3(2):199–230, 1995.

    Article  Google Scholar 

  13. J. Peña, J. Lozano, and P. Larrañaga. An empirical comparison of four initialization methods for the k-means algorithm. Pattern Recognition Letters, 20:1027–1040,1999.

    Article  Google Scholar 

  14. J.R. Quinlan. C4.5: Programs for machine learning. Morgan Kaufmann, 1993.

    Google Scholar 

  15. D. Palomo van Es. Fuzzy association rules and promotional sales data. Master’s thesi Leiden University, 2001.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Leiden Institute of Advanced Computer Science, Universiteit Leiden, 2300 RA Leiden, P.O. Box 9512, The Netherlands

    Jeroen Eggermont

Authors
  1. Jeroen Eggermont
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Idaho, 83844-1010, Moscow, ID, USA

    James A. Foster

  2. INRIA Rocquencourt, B.P. 105, 78153, Le Chesnay Cedex, France

    Evelyne Lutton

  3. School of Computer Science, University of Birmingham, B15 2TT, Birmingham, UK

    Julian Miller

  4. Department of Computer Science and Information Systems, University of Limerick, Schuman Building, Limerick, Ireland

    Conor Ryan

  5. Dip. Tecnologie dell’Informazione, Università degli Studi di Milano, Via Bramante 65, 26013, Crema, Italy

    Andrea Tettamanzi

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Eggermont, J. (2002). Evolving Fuzzy Decision Trees with Genetic Programming and Clustering. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A. (eds) Genetic Programming. EuroGP 2002. Lecture Notes in Computer Science, vol 2278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45984-7_7

Download citation

  • DOI: https://doi.org/10.1007/3-540-45984-7_7

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43378-1

  • Online ISBN: 978-3-540-45984-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation