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.

中文翻译：

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L1惩罚ROC曲线优化支持向量机的正则化路径

接收者操作特征 (ROC) 曲线是评估二元分类器在各种应用中的性能的最流行工具之一。Rakotomamonjy (2004) 提出了直接优化 ROC 曲线下面积而不是预测精度的 ROC-SVM。在本文中，我们研究了直接优化 ROC 曲线的L ₁ -penalized ROC-SVM。我们首先证明L ₁惩罚的 ROC-SVM 具有分段线性正则化路径，然后开发了一种有效的算法来计算整个路径，这极大地促进了其调整过程。