Skip to main content
Springer Nature Link
Log in

Predicting the Relationship Between the Size of Training Sample and the Predictive Power of Classifiers

  • Conference paper
  • First Online:
Knowledge-Based Intelligent Information and Engineering Systems (KES 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3215))

Abstract

The main objective of this paper is to investigate the relationship between the size of training sample and the predictive power of well-known classification techniques. We first display this relationship using the results of some empirical studies and then propose a general mathematical model which can explain this relationship. Next, we validate this model on some real data sets and found that the model provides a good fit to the data. This model also allow a more objective determination of optimum training sample size in contrast to current training sample size selection approaches which tend to be ad hoc or subjective.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Freund, Y.: An adaptive version of the boosting by majority algorithm. Machine Learning 43(3), 293–318 (2001)

    Article  Google Scholar 

  2. Schapire, R.E.: Drifting games. Machine Learning 43(3), 265–291 (2001)

    Article  Google Scholar 

  3. Ueda, N.: Optimal linear combination of neural networks for improving classification performance. Proceedings of IEEE Transactions on Pattern Analysis and Machine Intelligence 22(2), 207–215 (2000)

    Article  Google Scholar 

  4. Webb, G.I.: MultiBoosting: a technique for combining boosting and wagging. Machine Learning 40(2), 159–196 (2000)

    Article  Google Scholar 

  5. Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. Proceedings of IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)

    Article  Google Scholar 

  6. Weiss, S.M., Indurkhya, N.: Predictive Data Mining: A Practical Guide. Morgan Kaufmann Publishers, California (1998)

    MATH  Google Scholar 

  7. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers, California (2000)

    Google Scholar 

  8. Groth, R.: Data Mining: Building Competitive Advantage. Prentice Hall, New Jersey (2000)

    Google Scholar 

  9. Berry, M.J.A., Linoff, G.: Data Mining Techniques For Marketing, Sales and Customer Support. John Wiley & Sons, New York (1997)

    Google Scholar 

  10. Lewis, E.M.: An Introduction to Credit Scoring. Athena Press, California (1992)

    Google Scholar 

  11. Valiant, L.G.: A theory of the learnable. Communications of the ACM 27, 1134–1142 (1984)

    Article  Google Scholar 

  12. Schapire, R.E.: The strength of weak learnability. Machine Learning 5, 197–227 (1990)

    Google Scholar 

  13. Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Statistics, RMIT University, Melbourne, Australia

    Natthaphan Boonyanunta & Panlop Zeephongsekul

Authors
  1. Natthaphan Boonyanunta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Panlop Zeephongsekul
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. KES International, 2nd Floor, 145-157 St John Street, EC1V 4PY, London, United Kingdom

    Mircea Gh. Negoita

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, 5095, SA, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Boonyanunta, N., Zeephongsekul, P. (2004). Predicting the Relationship Between the Size of Training Sample and the Predictive Power of Classifiers. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30134-9_71

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30134-9_71

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23205-6

  • Online ISBN: 978-3-540-30134-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics