title: 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.
authors: Department of Mathematical Informatics, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan takeda@mist.i.u-tokyo.ac.jp.
authors: Akiko
authors: A
authors: Takeda
authors: Shuhei
authors: S
authors: Fujiwara
authors: Takafumi
authors: T
authors: Kanamori
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005376:Financial Management; D006801:Humans; D008962:Models, Theoretical; D040242:Risk Reduction Behavior; D060388:Support Vector Machine