Value-at-risk support vector machine: stability to outliers

Peter Tsyurmasto (), Michael Zabarankin () and Stan Uryasev ()
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Peter Tsyurmasto: University of Florida
Michael Zabarankin: Stevens Institute of Technology
Stan Uryasev: University of Florida

Journal of Combinatorial Optimization, 2014, vol. 28, issue 1, No 12, 218-232

Abstract: Abstract A support vector machine (SVM) stable to data outliers is proposed in three closely related formulations, and relationships between those formulations are established. The SVM is based on the value-at-risk (VaR) measure, which discards a specified percentage of data viewed as outliers (extreme samples), and is referred to as $$\mathrm{VaR}$$ VaR -SVM. Computational experiments show that compared to the $$\nu $$ ν -SVM, the VaR-SVM has a superior out-of-sample performance on datasets with outliers.

Keywords: Support vector machine; Classification; Conditional value-at-risk; Value-at-risk; Risk management; Optimization (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s10878-013-9678-9

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