TY - JOUR
T1 - The regularization paths for the ROC-optimizing support vector machines
AU - Kim, Dohyun
AU - Shin, Seung Jun
N1 - Funding Information:
This work is supported by National Research Foundation of Korea Grant (NRF-2018R1D1A1B07043034).
Publisher Copyright:
© 2020, Korean Statistical Society.
PY - 2020/3/1
Y1 - 2020/3/1
N2 - Rakotomamonjy (RO-CAI, pp 71–80, 2009) proposed an ROC-SVM that optimizes the receiver operating characteristic curve (ROC) particularly useful for unbalanced classification. In this article, we establish the piecewise linearity of the ROC-SVM solutions as a function of regularization parameter, and develop an efficient algorithm for computing the entire regularization paths of the ROC-SVM. Finally we develop an R package, rocsvm.path, now available in CRAN.
AB - Rakotomamonjy (RO-CAI, pp 71–80, 2009) proposed an ROC-SVM that optimizes the receiver operating characteristic curve (ROC) particularly useful for unbalanced classification. In this article, we establish the piecewise linearity of the ROC-SVM solutions as a function of regularization parameter, and develop an efficient algorithm for computing the entire regularization paths of the ROC-SVM. Finally we develop an R package, rocsvm.path, now available in CRAN.
KW - Piecewise linearity
KW - Receiver operating characteristic curve
KW - Support vector machine
KW - Unbalanced classification
UR - http://www.scopus.com/inward/record.url?scp=85079762302&partnerID=8YFLogxK
U2 - 10.1007/s42952-019-00017-9
DO - 10.1007/s42952-019-00017-9
M3 - Article
AN - SCOPUS:85079762302
SN - 1226-3192
VL - 49
SP - 264
EP - 275
JO - Journal of the Korean Statistical Society
JF - Journal of the Korean Statistical Society
IS - 1
ER -