论文标题
基于AUC的选择性分类
AUC-based Selective Classification
论文作者
论文摘要
选择性分类（或使用拒绝选项分类）将分类器与选择功能配对，以确定是否应接受预测。该框架可以通过预测性能进行覆盖范围（接受预测的概率），通常通过分配损耗函数来衡量。在许多应用程序方案（例如信用评分）中，绩效是通过排名指标来衡量的，例如ROC曲线下的区域（AUC）。我们提出了一种模型 - 不合Snostic方法，将选择函数与给定的概率二进制分类器相关联。该方法专门针对优化AUC。我们提供理论上的理由和一种新颖的算法（称为Aucross）来实现这样的目标。实验表明，我们的方法成功地覆盖了AUC，改善了针对优化准确性的现有选择性分类方法。
Selective classification (or classification with a reject option) pairs a classifier with a selection function to determine whether or not a prediction should be accepted. This framework trades off coverage (probability of accepting a prediction) with predictive performance, typically measured by distributive loss functions. In many application scenarios, such as credit scoring, performance is instead measured by ranking metrics, such as the Area Under the ROC Curve (AUC). We propose a model-agnostic approach to associate a selection function to a given probabilistic binary classifier. The approach is specifically targeted at optimizing the AUC. We provide both theoretical justifications and a novel algorithm, called AUCROSS, to achieve such a goal. Experiments show that our method succeeds in trading-off coverage for AUC, improving over existing selective classification methods targeted at optimizing accuracy.
