论文标题
高维分类中的局部自适应收缩方法，用于错误的选择率控制
A Locally Adaptive Shrinkage Approach to False Selection Rate Control in High-Dimensional Classification
论文作者
论文摘要
在许多高导致决策情况下，分类器的不确定性量化和误差控制至关重要。我们提出了一个选择性分类框架，该框架为任何无法自信地分类的观察结果提供了犹豫不决的选项。虚假选择率（FSR）定义为所有确定分类中错误分类的预期分数，提供了一个有用的错误率概念，该概念将induckisions的一部分交易成更少的分类错误。在高维线性判别分析（LDA）的背景下，我们为FSR控制开发了一类新的本地自适应收缩和选择（LASS）规则。 LASS易于分析，并且在稀疏和密集的政权中表现出色。根据高维LDA中现有理论要求，建立了FSR控制的理论保证，而没有对稀疏性的强烈假设。使用模拟和真实数据研究了劳斯的经验表现。
The uncertainty quantification and error control of classifiers are crucial in many high-consequence decision-making scenarios. We propose a selective classification framework that provides an indecision option for any observations that cannot be classified with confidence. The false selection rate (FSR), defined as the expected fraction of erroneous classifications among all definitive classifications, provides a useful error rate notion that trades off a fraction of indecisions for fewer classification errors. We develop a new class of locally adaptive shrinkage and selection (LASS) rules for FSR control in the context of high-dimensional linear discriminant analysis (LDA). LASS is easy-to-analyze and has robust performance across sparse and dense regimes. Theoretical guarantees on FSR control are established without strong assumptions on sparsity as required by existing theories in high-dimensional LDA. The empirical performances of LASS are investigated using both simulated and real data.
