Adjusted support vector machines based on a new loss function

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

13 Scopus Citations
View graph of relations

Author(s)

  • Shuchun Wang
  • Wei Jiang
  • Kwok-Leung Tsui

Detail(s)

Original languageEnglish
Pages (from-to)83-101
Journal / PublicationAnnals of Operations Research
Volume174
Issue number1
Publication statusPublished - Feb 2010
Externally publishedYes

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. © Springer Science+Business Media, LLC 2008.

Research Area(s)

  • Classification error, Cross validation, Dispersion, Sampling bias

Citation Format(s)

Adjusted support vector machines based on a new loss function. / Wang, Shuchun; Jiang, Wei; Tsui, Kwok-Leung.

In: Annals of Operations Research, Vol. 174, No. 1, 02.2010, p. 83-101.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review