The regularization paths for the ROC-optimizing support vector machines

Dohyun Kim; Seung Jun Shin

doi:10.1007/s42952-019-00017-9

The regularization paths for the ROC-optimizing support vector machines

Dohyun Kim
, Seung Jun Shin^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	264-275
Number of pages	12
Journal	Journal of the Korean Statistical Society
Volume	49
Issue number	1
DOIs	https://doi.org/10.1007/s42952-019-00017-9
Publication status	Published - 2020 Mar 1

Bibliographical note

Funding Information:
This work is supported by National Research Foundation of Korea Grant (NRF-2018R1D1A1B07043034).

Publisher Copyright:
© 2020, Korean Statistical Society.

Keywords

Piecewise linearity
Receiver operating characteristic curve
Support vector machine
Unbalanced classification

ASJC Scopus subject areas

Statistics and Probability

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10.1007/s42952-019-00017-9

Cite this

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title = "The regularization paths for the ROC-optimizing support vector machines",

abstract = "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.",

keywords = "Piecewise linearity, Receiver operating characteristic curve, Support vector machine, Unbalanced classification",

author = "Dohyun Kim and Shin, \{Seung Jun\}",

note = "Funding Information: This work is supported by National Research Foundation of Korea Grant (NRF-2018R1D1A1B07043034). Publisher Copyright: {\textcopyright} 2020, Korean Statistical Society.",

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AU - Shin, Seung Jun

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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 - https://www.scopus.com/pages/publications/85079762302

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IS - 1

ER -

The regularization paths for the ROC-optimizing support vector machines

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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