Regularization paths of L1-penalized ROC Curve-Optimizing Support Vector Machines

Hyungwoo Kim, Insuk Sohn, Seung Jun Shin

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


The receiver operator characteristic (ROC) curve is one of the most popular tools to evaluate the performance of binary classifiers in a variety of applications. Rakotomamonjy (2004) proposed the ROC-SVM that directly optimizes the area under the ROC curve instead of the prediction accuracy. In this article, we study the L1-penalized ROC-SVM that directly optimizes the ROC curve. We first show that the L1-penalized ROC-SVM has piecewise linear regularization paths and then develop an efficient algorithm to compute the entire paths, which greatly facilitates its tuning procedure.

Original languageEnglish
Article numbere400
Issue number1
Publication statusPublished - 2021 Dec

Bibliographical note

Funding Information:
Kim and Shin's work is partially funded by the National Research Foundation of Korea (NRF) Grants 2018R1D1A1B07043034 and 2019R1A4A1028134.

Publisher Copyright:
© 2021 John Wiley & Sons, Ltd.

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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