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Adjusted support vector machines based on a new loss function

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  • Shuchun Wang
  • Wei Jiang
  • Kwok-Leung Tsui

Abstract

Support vector machine (SVM) has attracted considerable attentions recently due to its successful applications in various domains. However, by maximizing the margin of separation between the two classes in a binary classification problem, the SVM solutions often suffer two serious drawbacks. First, SVM separating hyperplane is usually very sensitive to training samples since it strongly depends on support vectors which are only a few points located on the wrong side of the corresponding margin boundaries. Second, the separating hyperplane is equidistant to the two classes which are considered equally important when optimizing the separating hyperplane location regardless the number of training data and their dispersions in each class. In this paper, we propose a new SVM solution, adjusted support vector machine (ASVM), based on a new loss function to adjust the SVM solution taking into account the sample sizes and dispersions of the two classes. Numerical experiments show that the ASVM outperforms conventional SVM, especially when the two classes have large differences in sample size and dispersion. Copyright Springer Science+Business Media, LLC 2010

Suggested Citation

  • Shuchun Wang & Wei Jiang & Kwok-Leung Tsui, 2010. "Adjusted support vector machines based on a new loss function," Annals of Operations Research, Springer, vol. 174(1), pages 83-101, February.
    • Handle: RePEc:spr:annopr:v:174:y:2010:i:1:p:83-101:10.1007/s10479-008-0495-y
    DOI: 10.1007/s10479-008-0495-y
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    References listed on IDEAS

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    1. Peter Hall & J. S. Marron & Amnon Neeman, 2005. "Geometric representation of high dimension, low sample size data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(3), pages 427-444, June.
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    Cited by:

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    2. Shuguang He & Wei Jiang & Houtao Deng, 2018. "A distance-based control chart for monitoring multivariate processes using support vector machines," Annals of Operations Research, Springer, vol. 263(1), pages 191-207, April.
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    4. Ayşegül Aşkan & Serpil Sayın, 2014. "SVM classification for imbalanced data sets using a multiobjective optimization framework," Annals of Operations Research, Springer, vol. 216(1), pages 191-203, May.
    5. Pablo Aparicio-Ruiz & Elena Barbadilla-Martín & José Guadix & Pablo Cortés, 2021. "KNN and adaptive comfort applied in decision making for HVAC systems," Annals of Operations Research, Springer, vol. 303(1), pages 217-231, August.

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