Extended Robust Support Vector Machine Based on Financial Risk Minimization

Abstract

Financial risk measures have been used recently in machine learning. For example, ν-support vector machine ν-SVM) minimizes the conditional value at risk (CVaR) of margin distribution. The measure is popular in finance because of the subadditivity property, but it is very sensitive to a few outliers in the tail of the distribution. We propose a new classification method, extended robust SVM (ER-SVM), which minimizes an intermediate risk measure between the CVaR and value at risk (VaR) by expecting that the resulting model becomes less sensitive than ν-SVM to outliers. We can regard ER-SVM as an extension of robust SVM, which uses a truncated hinge loss. Numerical experiments imply the ER-SVM's possibility of achieving a better prediction performance with proper parameter setting.

DOI: 10.1162/NECO_a_00647

Cite this paper

@article{Takeda2014ExtendedRS, title={Extended Robust Support Vector Machine Based on Financial Risk Minimization}, author={Akiko Takeda and Shuhei Fujiwara and Takafumi Kanamori}, journal={Neural computation}, year={2014}, volume={26 11}, pages={2541-69} }