Neyman-Pearson classification algorithms and NP receiver operating characteristics.
Neyman-Pearson classification algorithms and NP receiver operating characteristics.
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DOI：
10.1126/sciadv.aao1659
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发表时间：
2018-03
期刊：
影响因子：
13.6
通讯作者：
Li JJ
中科院分区：
文献类型：
--
作者：
Tong X;Feng Y;Li JJ
二进制分类中非对称错误控制的伞形算法和图形工具。在许多二进制分类应用中，例如疾病诊断和垃圾邮件检测，从业者通常需要限制I类错误（即，将0类观察错误分类为1类的条件概率），以使其保持在期望的阈值以下。为了满足这一需求，Neyman-Pearson（NP）分类范式是一个自然的选择;它最大限度地减少了II型错误（即，将1类观测错误分类为0类的条件概率），同时对I型错误强制执行上限α。尽管NP范式在假设检验方面有着长达一个世纪的历史，但它在分类方案中并没有得到很好的认可和实施。直接将经验I类错误限制在不超过α的常见做法并不满足I类错误控制目标，因为所得分类器的I类错误可能远远大于α，并且NP范式在实践中没有得到适当的实施。我们开发了第一个伞形算法，该算法实现了所有评分类型分类方法的NP范式，如逻辑回归，支持向量机和随机森林。在该算法的支持下，我们提出了一种新的NP分类方法的图形工具：受试者工作特征（NP-ROC）带受欢迎的ROC曲线。NP-ROC带将有助于以数据自适应的方式选择α并比较不同的NP分类器。我们展示了NP伞算法和NP-ROC波段的使用和属性，可在R包nproc中，通过模拟和真实的数据研究。
An umbrella algorithm and a graphical tool for asymmetric error control in binary classification. In many binary classification applications, such as disease diagnosis and spam detection, practitioners commonly face the need to limit type I error (that is, the conditional probability of misclassifying a class 0 observation as class 1) so that it remains below a desired threshold. To address this need, the Neyman-Pearson (NP) classification paradigm is a natural choice; it minimizes type II error (that is, the conditional probability of misclassifying a class 1 observation as class 0) while enforcing an upper bound, α, on the type I error. Despite its century-long history in hypothesis testing, the NP paradigm has not been well recognized and implemented in classification schemes. Common practices that directly limit the empirical type I error to no more than α do not satisfy the type I error control objective because the resulting classifiers are likely to have type I errors much larger than α, and the NP paradigm has not been properly implemented in practice. We develop the first umbrella algorithm that implements the NP paradigm for all scoring-type classification methods, such as logistic regression, support vector machines, and random forests. Powered by this algorithm, we propose a novel graphical tool for NP classification methods: NP receiver operating characteristic (NP-ROC) bands motivated by the popular ROC curves. NP-ROC bands will help choose α in a data-adaptive way and compare different NP classifiers. We demonstrate the use and properties of the NP umbrella algorithm and NP-ROC bands, available in the R package nproc, through simulation and real data studies.
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