Informatics Engineering and Information Science

Volume 252 of the series Communications in Computer and Information Science pp 677-690

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

  • Djeffal AbdelhamidAffiliated withComputer Science Department, LESIA Laboratory, Biskra University
  • , Babahenini Mohamed ChaoukiAffiliated withComputer Science Department, LESIA Laboratory, Biskra University
  • , Taleb-Ahmed AbdelmalikAffiliated withLAMIH Laboratory FRE CNRS 3304 UVHC, Valenciennes University

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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.


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