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Adjusted Estimation for the Combination of Classifiers

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Advances in Intelligent Data Analysis (IDA 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1642))

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Abstract

An algorithm is proposed which adaptively and simultaneously estimates and combines classifiers originating from distinct classiffication frameworks for improved prediction. The methodology is developed and evaluated on simulations and real data. Analogies and similarities with generalised additive modeling, neural estimation and boosting are discussed. We contrast the approach with existing Bayesian model averaging methods. Areas for further research and development are indicated.

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Author information

Authors and Affiliations

  1. Trinity College Dublin, Dublin 2, Ireland

    Bart J. A. Mertens

  2. Imperial College, London, SW7 2BZ, UK

    David J. Hand

Authors
  1. Bart J. A. Mertens
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  2. David J. Hand
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Editor information

Editors and Affiliations

  1. Department of Mathematics, Imperial College, Huxley Building 180 Queen’s Gate, London, SW7 2BZ, UK

    David J. Hand

  2. Leiden Institute for Advanced Computer Science, Leiden University, 2300, RA Leiden, The Netherlands

    Joost N. Kok

  3. Berkeley Initiative in Soft Computing, University of California at Berkeley, 329 Soda Hall, Berkeley, CA, 94720, USA

    Michael R. Berthold

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© 1999 Springer-Verlag Berlin Heidelberg

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Mertens, B.J.A., Hand, D.J. (1999). Adjusted Estimation for the Combination of Classifiers. In: Hand, D.J., Kok, J.N., Berthold, M.R. (eds) Advances in Intelligent Data Analysis. IDA 1999. Lecture Notes in Computer Science, vol 1642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48412-4_27

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  • DOI: https://doi.org/10.1007/3-540-48412-4_27

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66332-4

  • Online ISBN: 978-3-540-48412-7

  • eBook Packages: Springer Book Archive

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