A New Approach to Multi-class SVM Learning Based on OC-SVM for Huge Databases

  • Djeffal Abdelhamid
  • Babahenini Mohamed Chaouki
  • Taleb-Ahmed Abdelmalik
Conference paper

DOI: 10.1007/978-3-642-25453-6_57

Part of the Communications in Computer and Information Science book series (CCIS, volume 252)
Cite this paper as:
Abdelhamid D., Mohamed Chaouki B., Abdelmalik TA. (2011) A New Approach to Multi-class SVM Learning Based on OC-SVM for Huge Databases. In: Abd Manaf A., Zeki A., Zamani M., Chuprat S., El-Qawasmeh E. (eds) Informatics Engineering and Information Science. ICIEIS 2011. Communications in Computer and Information Science, vol 252. Springer, Berlin, Heidelberg

Abstract

In this paper, we propose a new learning method for multi-class support vector machines based on single class SVM learning method. Unlike the methods 1vs1 and 1vsR, used in the literature and mainly based on binary SVM method, our method learns a classifier for each class from only its samples and then uses these classifiers to obtain a multiclass decision model. To enhance the accuracy of our method, we build from the obtained hyperplanes new hyperplanes, similar to those of the 1vsR method, for use in classification. Our method represents a considerable improvement in the speed of training and classification as well the decision model size while maintaining the same accuracy as other methods.

Keywords

Support vector machine Multiclass SVM One-class SVM 1vs1 1vsR 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Djeffal Abdelhamid
    • 1
  • Babahenini Mohamed Chaouki
    • 1
  • Taleb-Ahmed Abdelmalik
    • 2
  1. 1.Computer Science Department, LESIA LaboratoryBiskra UniversityAlgeria
  2. 2.LAMIH Laboratory FRE CNRS 3304 UVHCValenciennes UniversityFrance

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