铰链损耗
稳健性(进化)
最大化
数学优化
数学
凸性
公制(单位)
计算机科学
支持向量机
人工智能
运营管理
生物化学
基因
金融经济学
经济
化学
作者
Wenbo Ma,Miguel A. Lejeune
标识
DOI:10.1016/j.orl.2020.05.012
摘要
Area under ROC curve (AUC) is a widely used performance measure for classification models. We propose two new distributionally robust AUC maximization models (DR-AUC) that rely on the Kantorovich metric and approximate the AUC with the hinge loss function. We consider the two cases with respectively fixed and variable support for the worst-case distribution. We use duality theory to reformulate the DR-AUC models and derive tractable convex optimization problems. The numerical experiments show that the proposed DR-AUC models -- benchmarked with the standard deterministic AUC and the support vector machine models - perform better in general and in particular improve the worst-case out-of-sample performance over the majority of the considered datasets, thereby showing their robustness. The results are particularly encouraging since our numerical experiments are conducted with training sets of small size which have been known to be conducive to low out-of-sample performance.
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