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Parallel Tuning of Support Vector Machine Learning Parameters for Large and Unbalanced Data Sets

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Computational Life Sciences (CompLife 2005)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 3695))

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Abstract

We consider the problem of selecting and tuning learning parameters of support vector machines, especially for the classification of large and unbalanced data sets. We show why and how simple models with few parameters should be refined and propose an automated approach for tuning the increased number of parameters in the extended model. Based on a sensitive quality measure we analyze correlations between the number of parameters, the learning cost and the performance of the trained SVM in classifying independent test data. In addition we study the influence of the quality measure on the classification performance and compare the behavior of serial and asynchronous parallel parameter tuning on an IBM p690 cluster.

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Author information

Authors and Affiliations

  1. John von Neumann Institute for Computing, Central Institute for Applied Mathematics, Research Centre Juelich, Germany

    Tatjana Eitrich

  2. Applied Computer Science and Scientific Computing Group, Department of Mathematics, University of Wuppertal, Germany

    Bruno Lang

Authors
  1. Tatjana Eitrich
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  2. Bruno Lang
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Editor information

Editors and Affiliations

  1. Berkeley Initiative in Soft Computing (BISC), University of California at Berkeley, USA

    Michael R. Berthold

  2. Unilever Centre for Molecular Informatics, Department of Chemistry, Cambridge University, CB2 1EW, Cambridge, UK

    Robert C. Glen

  3. Department of Biology, University of Konstanz, Box M647, 78457, Konstanz, Germany

    Kay Diederichs

  4. Division for Simulation of Biological Systems, Center for Bioinformatics Tübingen, Eberhard Karls University Tübingen, 72076, Tübingen, Germany

    Oliver Kohlbacher

  5. ALTANA Chair for Bioinformatics and Information Mining, University of Konstanz, Germany

    Ingrid Fischer

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© 2005 Springer-Verlag Berlin Heidelberg

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Eitrich, T., Lang, B. (2005). Parallel Tuning of Support Vector Machine Learning Parameters for Large and Unbalanced Data Sets. In: R. Berthold, M., Glen, R.C., Diederichs, K., Kohlbacher, O., Fischer, I. (eds) Computational Life Sciences. CompLife 2005. Lecture Notes in Computer Science(), vol 3695. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11560500_23

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  • DOI: https://doi.org/10.1007/11560500_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29104-6

  • Online ISBN: 978-3-540-31726-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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