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Data Mining with Products of Trees

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Advances in Intelligent Data Analysis (IDA 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2189))

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Abstract

We propose a new model for supervised classification for data mining applications. This model is based on products of trees. The information given by each predictor variable is separately extracted by means of a recursive partition structure. This information is then combined across predictors using a weighted product model form, an extension of the naive Bayes model. Empirical results are presented comparing this new method with other methods in the machine learning literature, for several data sets. Two typical data mining applications, a chromosome identification problem and a forest cover type identification problem are used to illustrate the ideas. The new approach is fast and surprisingly accurate.

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Author information

Authors and Affiliations

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

    José Tomé A.S. Ferreira, David G.T. Denison & David J. Hand

Authors
  1. José Tomé A.S. Ferreira
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  2. David G.T. Denison
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  3. David J. Hand
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Editor information

Editors and Affiliations

  1. Royal Institute of Technology, Centre for Autonomous Systems, 10044, Stockholm, Sweden

    Frank Hoffmann

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

    David J. Hand  & Niall Adams  & 

  3. Department of Computer Science, Vanderbilt University, Box 1679, Station B, Nashville, TN, 37235, USA

    Douglas Fisher

  4. Department of Computer Science, New University of Lisbon, 2825-114, Caparica, Portugal

    Gabriela Guimaraes

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© 2001 Springer-Verlag Berlin Heidelberg

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Ferreira, J.T.A., Denison, D.G., Hand, D.J. (2001). Data Mining with Products of Trees. In: Hoffmann, F., Hand, D.J., Adams, N., Fisher, D., Guimaraes, G. (eds) Advances in Intelligent Data Analysis. IDA 2001. Lecture Notes in Computer Science, vol 2189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44816-0_17

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  • DOI: https://doi.org/10.1007/3-540-44816-0_17

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42581-6

  • Online ISBN: 978-3-540-44816-7

  • eBook Packages: Springer Book Archive

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