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Combining One-Versus-One and One-Versus-All Strategies to Improve Multiclass SVM Classifier

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Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 403))

Abstract

Support Vector Machine (SVM) is a binary classifier, but most of the problems we find in the real-life applications are multiclass. There are many methods of decomposition such a task into the set of smaller classification problems involving two classes only. Two of the widely known are one-versus-one and one-versus-rest strategies. There are several papers dealing with these methods, improving and comparing them. In this paper, we try to combine theses strategies to exploit their strong aspects to achieve better performance. As the performance we understand both recognition ratio and the speed of the proposed algorithm. We used SVM classifier on several different databases to test our solution. The results show that we obtain better recognition ratio on all tested databases. Moreover, the proposed method turns out to be much more efficient than the original one-versus-one strategy.

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Author information

Authors and Affiliations

  1. Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Łojasiewicza 11, 30-348, Krakow, Poland

    Wiesław Chmielnicki

  2. Institute of Computer Science, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Katarzyna Sta̧por

Authors
  1. Wiesław Chmielnicki
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  2. Katarzyna Sta̧por
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Corresponding author

Correspondence to Wiesław Chmielnicki .

Editor information

Editors and Affiliations

  1. Department of Systems, Wrocław University of Technology, Wroclaw, Poland

    Robert Burduk

  2. Department of Systems and Computer, Wrocław University of Technology, Wroclaw, Poland

    Konrad Jackowski

  3. Department of Systems and Computer, Wrocław University of Technology, Wroclaw, Poland

    Marek Kurzyński

  4. Dept. of Systems and Computer Networks, Wrocław University of Technology, Wroclaw, Poland

    Michał Woźniak

  5. Department of Systems, Wrocław University of Technology, Wroclaw, Poland

    Andrzej Żołnierek

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Chmielnicki, W., Sta̧por, K. (2016). Combining One-Versus-One and One-Versus-All Strategies to Improve Multiclass SVM Classifier. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-26227-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26225-3

  • Online ISBN: 978-3-319-26227-7

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