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.
Bibliographical noteFunding Information:
Kim and Shin's work is partially funded by the National Research Foundation of Korea (NRF) Grants 2018R1D1A1B07043034 and 2019R1A4A1028134.
© 2021 John Wiley & Sons, Ltd.
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty