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A comparative study on large scale kernelized support vector machines

Author

Listed:
  • Daniel Horn

    (Technische Universität Dortmund)

  • Aydın Demircioğlu

    (Ruhr-Universität Bochum)

  • Bernd Bischl

    (LMU München)

  • Tobias Glasmachers

    (Ruhr-Universität Bochum)

  • Claus Weihs

    (Technische Universität Dortmund)

Abstract

Kernelized support vector machines (SVMs) belong to the most widely used classification methods. However, in contrast to linear SVMs, the computation time required to train such a machine becomes a bottleneck when facing large data sets. In order to mitigate this shortcoming of kernel SVMs, many approximate training algorithms were developed. While most of these methods claim to be much faster than the state-of-the-art solver LIBSVM, a thorough comparative study is missing. We aim to fill this gap. We choose several well-known approximate SVM solvers and compare their performance on a number of large benchmark data sets. Our focus is to analyze the trade-off between prediction error and runtime for different learning and accuracy parameter settings. This includes simple subsampling of the data, the poor-man’s approach to handling large scale problems. We employ model-based multi-objective optimization, which allows us to tune the parameters of learning machine and solver over the full range of accuracy/runtime trade-offs. We analyze (differences between) solvers by studying and comparing the Pareto fronts formed by the two objectives classification error and training time. Unsurprisingly, given more runtime most solvers are able to find more accurate solutions, i.e., achieve a higher prediction accuracy. It turns out that LIBSVM with subsampling of the data is a strong baseline. Some solvers systematically outperform others, which allows us to give concrete recommendations of when to use which solver.

Suggested Citation

  • Daniel Horn & Aydın Demircioğlu & Bernd Bischl & Tobias Glasmachers & Claus Weihs, 2018. "A comparative study on large scale kernelized support vector machines," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(4), pages 867-883, December.
  • Handle: RePEc:spr:advdac:v:12:y:2018:i:4:d:10.1007_s11634-016-0265-7
    DOI: 10.1007/s11634-016-0265-7
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    References listed on IDEAS

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    1. Bischl, Bernd & Lang, Michel & Mersmann, Olaf & Rahnenführer, Jörg & Weihs, Claus, 2015. "BatchJobs and BatchExperiments: Abstraction Mechanisms for Using R in Batch Environments," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i11).
    2. Joachims, Thorsten, 1998. "Making large-scale SVM learning practical," Technical Reports 1998,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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    Cited by:

    1. Behzad Pirouz & Behrouz Pirouz, 2023. "Multi-Objective Models for Sparse Optimization in Linear Support Vector Machine Classification," Mathematics, MDPI, vol. 11(17), pages 1-18, August.

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