Evaluating classification performance: Receiver operating characteristic and expected utility.
Evaluating classification performance: Receiver operating characteristic and expected utility.
复制标题
评估分类性能：接收器操作特性和预期效用。
DOI：
10.1037/met0000515
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发表时间：
2022
影响因子：
7
通讯作者：
Yang, Yueran
中科院分区：
文献类型：
--
作者：
Yang, Yueran
受试者工作特征（ROC）分析的一个主要优点被认为是其能够独立于诸如先验概率和分类结果的效用等因素来量化分类性能。本文的观点正好相反。在评估分类性能时，ROC分析应考虑先验概率和效用。本文通过建立期望效用线（EU线），揭示了分类器的ROC曲线与分类的期望效用之间的关系。特别是，EU线可以用来估计预期的效用时，分类器操作在任何ROC点的任何给定的先验概率和效用。EU线在所有情况下都是有用的-无论是检查单个分类器还是比较多个分类器，无论是比较分类器的潜力以最大化预期效用还是分类器的实际预期效用，以及ROC曲线是完整的还是部分的，连续的还是离散的。ROC和预期效用分析之间的联系揭示了这两种方法的共同目标：最大化分类的预期效用。特别地，ROC分析在选择最佳分类器及其最佳操作点以最大化期望效用方面是有用的。然而，选择分类器及其操作点（即改变条件概率）并不是增加预期效用的唯一方法。受估计期望效用所涉及的参数的启发，本文还讨论了ROC分析之外的其他提高期望效用的方法。（PsycInfo数据库记录（c）2022阿帕，保留所有权利）
One primary advantage of receiver operating characteristic (ROC) analysis is considered to be its ability to quantify classification performance independently of factors such as prior probabilities and utilities of classification outcomes. This article argues the opposite. When evaluating classification performance, ROC analysis should consider prior probabilities and utilities. By developing expected utility lines (EU lines), this article shows the connection between a classifier’s ROC curve and expected utility of classification. In particular, EU lines can be used to estimate expected utilities when classifiers operate at any ROC point for any given prior probabilities and utilities. EU lines are useful across all situations—no matter if one examines a single classifier or compares multiple classifiers, if one compares classifiers’ potential to maximize expected utilities or classifiers’ actual expected utilities, and if the ROC curves are full or partial, continuous or discrete. The connection between ROC and expected utility analyses reveals the common objective underlying these two methods: to maximize expected utility of classification. Particularly, ROC analysis is useful in choosing an optimal classifier and its optimal operating point to maximize expected utility. Yet, choosing a classifier and its operating point (ie, changing conditional probabilities) is not the only way to increase expected utility. Inspired by parameters involved in estimating expected utility, this article also discusses other approaches to increase expected utility beyond ROC analysis.(PsycInfo Database Record (c) 2022 APA, all rights reserved)