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

  • Wiesław ChmielnickiEmail author
  • Katarzyna Sta̧por
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 403)


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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Physics, Astronomy and Applied Computer ScienceJagiellonian UniversityKrakowPoland
  2. 2.Institute of Computer ScienceSilesian University of TechnologyGliwicePoland

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