无需标注校准数据，用无监督样本实现可靠分类置信集
Split Conformal Classification with Unsupervised Calibration
- 用未标注数据替代标注校准数据，降低标签成本
- 性能接近有监督校准，仅轻微降低置信度保证
- 适合标签稀缺、无法额外获取标注的场景
分割校准预测方法利用校准样本将任意预测规则转化为满足目标覆盖率的集合预测规则，现有方法在计算开销极低的情况下提供极强的性能保障。然而，这些方法要求校准样本必须由与训练数据不同的标注样本构成，这带来显著不便：既无法充分利用所有标注数据进行训练，又可能需要额外获取标签仅用于校准。本文提出一种针对分类任务的无监督校准分割校准方法，使用未标注校准样本与原有监督训练样本共同构建集合预测规则。理论与实验结果表明，该方法性能可媲美有监督校准，仅伴随适度的性能保证下降和计算效率损失。
原文摘要 · Abstract (English)
Methods for split conformal prediction leverage calibration samples to transform any prediction rule into a set-prediction rule that complies with a target coverage probability. Existing methods provide remarkably strong performance guarantees with minimal computational costs. However, they require to use calibration samples composed by labeled examples different to those used for training. This requirement can be highly inconvenient, as it prevents the use of all labeled examples for training and may require acquiring additional labels solely for calibration. This paper presents an effective methodology for split conformal prediction with unsupervised calibration for classification tasks. In the proposed approach, set-prediction rules are obtained using unsupervised calibration samples together with supervised training samples previously used to learn the classification rule. Theoretical and experimental results show that the presented methods can achieve performance comparable to that with supervised calibration, at the expenses of a moderate degradation in performance guarantees and computational efficiency.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。