提出新方法估算二分类置信区间，提升关键任务自动化可靠性。
Estimation of Confidence Bounds in Binary Classification using Wilson Score Kernel Density Estimation
- 用威尔逊分数核密度估计建模条件变化的成功概率
- 在四个数据集上表现接近高斯过程，但计算更轻量
- 适合需要可靠置信度的视觉基础模型下游任务
近年来，基于深度学习的二分类器性能与易用性显著提升，为自动化关键检测任务提供了可能。然而，此类分类器在关键操作中的应用依赖于可靠置信区间的估计，以确保系统性能达到给定统计显著性水平。本文提出威尔逊分数核密度分类（Wilson Score Kernel Density Classification），一种基于核的方法，用于二分类中置信区间的估计。其核心是威尔逊分数核密度估计器，可对具有条件变化成功概率的二项实验进行函数估计。该方法在四个不同数据集上的选择性分类任务中进行了评估，展示了作为任意特征提取器分类头的通用性，包括视觉基础模型。结果表明，该方法性能接近高斯过程分类，但计算复杂度更低。
原文摘要 · Abstract (English)
The performance and ease of use of deep learning-based binary classifiers have improved significantly in recent years. This has opened up the potential for automating critical inspection tasks, which have traditionally only been trusted to be done manually. However, the application of binary classifiers in critical operations depends on the estimation of reliable confidence bounds such that system performance can be ensured up to a given statistical significance. We present Wilson Score Kernel Density Classification, which is a novel kernel-based method for estimating confidence bounds in binary classification. The core of our method is the Wilson Score Kernel Density Estimator, which is a function estimator for estimating confidence bounds in Binomial experiments with conditionally varying success probabilities. Our method is evaluated in the context of selective classification on four different datasets, illustrating its use as a classification head of any feature extractor, including vision foundation models. Our proposed method shows similar performance to Gaussian Process Classification, but at a lower computational complexity.
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