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

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Book cover 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.

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Author information

Authors and Affiliations

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

    Natthaphan Boonyanunta & Panlop Zeephongsekul

Authors
  1. Natthaphan Boonyanunta
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  2. Panlop Zeephongsekul
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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

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© 2004 Springer-Verlag Berlin Heidelberg

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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

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

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23205-6

  • Online ISBN: 978-3-540-30134-9

  • eBook Packages: Springer Book Archive

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