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Fuzzy Semi-supervised Large Margin One-Class Support Vector Machine

  • Trung LeEmail author
  • Van Nguyen
  • Thien Pham
  • Mi Dinh
  • Thai Hoang Le
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 341)

Abstract

One-class Support Vector Machine (OCSVM) is one of state-of-the-art kernel-based methods for one-class classification problem. OCSVM produces the good performance for imbalanced dataset. Nonetheless, it cannot make use of negative data samples and also cannot utilize unlabeled data to boost the classifier. In this paper, we first extend the model of OCSVM to make use of the information carried by negative data samples for classification and then propose how to integrate the semi-supervised paradigm to the extended OCSVM for utilizing the unlabeled data to increase the classifier’s generalization ability. Finally, we show how to apply the fuzzy theory to the proposed semi-supervised one-class classification method for efficiently handling noises and outliers.

Keywords

One-class classification Novelty detection Support vector machine Semi-supervised learning S3VM Fuzzy membership 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Trung Le
    • 1
    Email author
  • Van Nguyen
    • 1
  • Thien Pham
    • 1
  • Mi Dinh
    • 1
  • Thai Hoang Le
    • 2
  1. 1.Faculty of Information TechnologyHCMc University of PedagogyHo Chi Minh CityVietnam
  2. 2.Faculty of Information TechnologyHCMc University of ScienceHo Chi Minh CityVietnam

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