论文标题
如何控制二进制分类器的错误率
How to Control the Error Rates of Binary Classifiers
论文作者
论文摘要
传统的二进制分类框架构建了可能具有良好精度的分类器，但它们的假正和错误的负错误率不在用户的控制之下。在许多情况下，其中一个错误更为严重，只有相应速率低于预定义阈值的分类器才可以接受。在这项研究中，我们将二元分类与统计假设检验结合在一起，以控制已训练的分类器的目标错误率。特别是，我们展示了如何将二进制分类器变成统计测试，计算分类p值并使用它们来限制目标错误率。
The traditional binary classification framework constructs classifiers which may have good accuracy, but whose false positive and false negative error rates are not under users' control. In many cases, one of the errors is more severe and only the classifiers with the corresponding rate lower than the predefined threshold are acceptable. In this study, we combine binary classification with statistical hypothesis testing to control the target error rate of already trained classifiers. In particular, we show how to turn binary classifiers into statistical tests, calculate the classification p-values, and use them to limit the target error rate.
