Regularization paths of L1‐penalized ROC Curve‐Optimizing Support Vector Machines
Hyungwoo Kim, Insuk Sohn, Seung Jun Shin · Stat · 2021
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.