Abstract
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 language | English |
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Article number | e400 |
Journal | Stat |
Volume | 10 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2021 Dec |
Bibliographical note
Publisher Copyright:© 2021 John Wiley & Sons, Ltd.
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty