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Robust Ensemble Learning for Data Mining

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Knowledge Discovery and Data Mining. Current Issues and New Applications (PAKDD 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1805))

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Abstract

We propose a new boosting algorithm which similarly to v-Support-Vector Classification allows for the possibility of a pre-specified fraction v of points to lie in the margin area or even on the wrong side of the decision boundary. It gives a nicely interpretable way of controlling the trade-off between minimizing training error and capacity. Furthermore, it can act as a filter for finding and selecting informative patterns from a database.

This paper is a short version of [8].

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Author information

Authors and Affiliations

  1. GMD FIRST, Kekuléstr. 7, D-12489, Berlin, Germany

    Gunnar Rätsch, Sebastian Mika & Klaus-Robert Müller

  2. Microsoft Research, 1 Guildhall Street, Cambridge, UK

    Bernhard Schölkopf

  3. Dep. of Engineering, ANU, Canberra, ACT, 0200, Australia

    Alexander Johannes Smola

  4. CIRL CRIEPI, 2-11-1 Iwadokita, Komae-shi, Tokyo, 201-8511, Japan

    Takashi Onoda

Authors
  1. Gunnar Rätsch
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  2. Bernhard Schölkopf
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  3. Alexander Johannes Smola
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  4. Sebastian Mika
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  5. Takashi Onoda
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  6. Klaus-Robert Müller
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Editor information

Editors and Affiliations

  1. Graduate School of Systems Management, Universiy of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, Tokyo, 112-0012, Japan

    Takao Terano

  2. Department of Computer Science and Engineering, Arizona State University, P.O. Box 875 406, Tempe, AZ, 85287-5406

    Huan Liu

  3. Department of Computer Science, National Tsing Hua University, Hsinchu, 300, Taiwan ROC

    Arbee L. P. Chen

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© 2000 Springer-Verlag Berlin Heidelberg

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Rätsch, G., Schölkopf, B., Smola, A.J., Mika, S., Onoda, T., Müller, KR. (2000). Robust Ensemble Learning for Data Mining. In: Terano, T., Liu, H., Chen, A.L.P. (eds) Knowledge Discovery and Data Mining. Current Issues and New Applications. PAKDD 2000. Lecture Notes in Computer Science(), vol 1805. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45571-X_39

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  • DOI: https://doi.org/10.1007/3-540-45571-X_39

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67382-8

  • Online ISBN: 978-3-540-45571-4

  • eBook Packages: Springer Book Archive

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