Maximization of AUC and Buffered AUC in Classification
Maximization of AUC and Buffered AUC in Classification
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分类中 AUC 和缓冲 AUC 的最大化
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
S. Uryasev
中科院分区：
文献类型：
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作者：
Matthew Norton;S. Uryasev
本文利用称为缓冲超出概率 (bPOE) 的新概念，引入了接收器工作特性曲线下面积 (AUC) 性能指标的替代方案，称为缓冲 AUC (bAUC)。 bAUC 创建的核心是计算和优化 bPOE 的新技术。我们证明这个公式可以很容易地集成到优化框架中，通常将 bPOE 最小化简化为凸规划，有时甚至是线性规划。然后，我们利用 bPOE 创建 bAUC 性能指标，表明它是 AUC 的直观对应物。此外，我们还表明，bAUC 在优化框架中比 AUC 更容易处理，特别是简化为凸规划和线性规划。我们使用这些友好的优化属性来引入 bAUC 效率前沿，这个概念可以部分解决需要考虑错误分类成本时出现的“不连贯性”。我们的结论是，bAUC 避免了 AUC 遇到的许多数值上麻烦的问题，并且更顺利地集成到模型选择和评估的总体框架中。
This paper utilizes a new concept, called Buffered Probability of Exceedance (bPOE), to introduce an alternative to the Area Under the Receiver Operating Characteristic Curve (AUC) performance metric called Buffered AUC (bAUC). Central to the creation of bAUC is a new technique for calculation and optimization of bPOE. We show this formula to be easily integrable into optimization frameworks, often reducing bPOE minimization to convex, sometimes even linear, programming. Then, we utilize bPOE to create the bAUC performance metric, showing it to be an intuitive counterpart to AUC. In addition, we show that bAUC is much easier to handle in optimization frameworks than AUC, specifically reducing to convex and linear programming. We use these friendly optimization properties to introduce the bAUC Efficiency Frontier, a concept that serves to partially resolve the “incoherency” that arises when misclassification costs need be considered. We conclude that bAUC avoids many of the numerically troublesome issues encountered by AUC and integrates much more smoothly into the general framework of model selection and evaluation.