Rare class classification by support vector machine

He He, Ali Ghodsi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The problem of classification on highly imbalanced datasets has been studied extensively in the literature. Most classifiers show significant deterioration in performance when dealing with skewed datasets. In this paper, we first examine the underlying reasons for SVM's deterioration on imbalanced datasets. We then propose two modifications for the soft margin SVM, where we change or add constraints to the optimization problem. The proposed methods are compared with regular SVM, cost-sensitive SVM and two re-sampling methods. Our experimental results demonstrate that this constrained SVM can consistently outperform the other associated methods.

Original languageEnglish (US)
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Pages548-551
Number of pages4
DOIs
StatePublished - 2010
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: Aug 23 2010Aug 26 2010

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other2010 20th International Conference on Pattern Recognition, ICPR 2010
CountryTurkey
CityIstanbul
Period8/23/108/26/10

Keywords

  • Classification
  • Novelty detection
  • Support vector machines

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    He, H., & Ghodsi, A. (2010). Rare class classification by support vector machine. In Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010 (pp. 548-551). [5597436] (Proceedings - International Conference on Pattern Recognition). https://doi.org/10.1109/ICPR.2010.139