Development and Evaluation of Cost-Sensitive Universum-SVM

Sauptik Dhar, Vladimir Cherkassky

Research output: Contribution to journalArticlepeer-review

58 Scopus citations


Many machine learning applications involve analysis of high-dimensional data, where the number of input features is larger than/comparable to the number of data samples. Standard classification methods may not be sufficient for such data, and this provides motivation for nonstandard learning settings. One such new learning methodology is called learning through contradiction or Universum-support vector machine (U-SVM). Recent studies have shown U-SVM to be quite effective for sparse high-dimensional data sets. However, all these earlier studies have used balanced data sets with equal misclassification costs. This paper extends the U-SVM formulation to problems with different misclassification costs, and presents practical conditions for the effectiveness of this cost-sensitive U-SVM. Several empirical comparisons are presented to validate the proposed approach.

Original languageEnglish (US)
Article number6911986
Pages (from-to)806-818
Number of pages13
JournalIEEE Transactions on Cybernetics
Issue number4
StatePublished - Apr 1 2015

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Cost-sensitive support vector machine (SVM)
  • Universum-SVM (U-SVM)
  • learning through contradiction
  • misclassification costs


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