Jeske Daniel R, Zhang Zhiwei, Smith Steven
Department of Statistics, University of California - Riverside, Riverside, CA, USA.
Division of Urology and Urological Oncology, City of Hope National Medical Center, Duarte, CA, USA.
Stat Methods Med Res. 2020 May;29(5):1420-1433. doi: 10.1177/0962280219863823. Epub 2019 Jul 18.
When the potential for making accurate classifications with a statistical classifier is limited, a neutral zone classifier can be constructed by adding a no-decision option as a classification outcome. We show how a neutral zone classifier can be constructed from a receiving operating characteristic (ROC) curve. We extend the ROC curve graphic to highlight important performance characteristics of a neutral zone classifier. Additional utility of neutral zone classifiers is illustrated by showing how they can be incorporated into the first stage of a two-stage classification process. At the first stage, a classification is attempted from easily collected or inexpensive features. If the classification falls into the neutral zone, additional relatively more expensive features can be obtained and used to make a definitive classification at the second stage. The methods discussed in the paper are illustrated with an application pertaining to prostate cancer.
当使用统计分类器进行准确分类的可能性有限时，可以通过添加一个不做决策的选项作为分类结果来构建一个中性区域分类器。我们展示了如何从接收操作特征（ROC）曲线构建中性区域分类器。我们扩展了ROC曲线图形以突出中性区域分类器的重要性能特征。通过展示如何将中性区域分类器纳入两阶段分类过程的第一阶段，说明了其额外的实用性。在第一阶段，尝试从易于收集或成本低廉的特征进行分类。如果分类落入中性区域，则可以获取额外的相对更昂贵的特征，并在第二阶段用于做出最终分类。本文讨论的方法通过一个与前列腺癌相关的应用进行了说明。
