TY - JOUR
T1 - Value-at-risk support vector machine
T2 - Stability to outliers
AU - Tsyurmasto, Peter
AU - Zabarankin, Michael
AU - Uryasev, Stan
PY - 2014/7
Y1 - 2014/7
N2 - 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 VaR -SVM. Computational experiments show that compared to the ν -SVM, the VaR-SVM has a superior out-of-sample performance on datasets with outliers.
AB - 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 VaR -SVM. Computational experiments show that compared to the ν -SVM, the VaR-SVM has a superior out-of-sample performance on datasets with outliers.
KW - Classification
KW - Conditional value-at-risk
KW - Optimization
KW - Risk management
KW - Support vector machine
KW - Value-at-risk
UR - http://www.scopus.com/inward/record.url?scp=84903595222&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84903595222&partnerID=8YFLogxK
U2 - 10.1007/s10878-013-9678-9
DO - 10.1007/s10878-013-9678-9
M3 - Article
AN - SCOPUS:84903595222
SN - 1382-6905
VL - 28
SP - 218
EP - 232
JO - Journal of Combinatorial Optimization
JF - Journal of Combinatorial Optimization
IS - 1
ER -