提出新方法提升高风险场景下结构化数据的异常检测准确率
Structure-Adaptive Conformal Inference for Large-Scale Out-of-Distribution Testing
- 基于结构自适应的置信度评分，融合个体证据与时空分组模式
- 在真实和模拟数据上实现错误发现率控制，且检验效能显著提升
- 适合需要可靠异常检测的医疗、金融等高风险领域
本文针对高风险机器学习应用中的结构化分布外（OOD）测试问题。传统合规方法依赖联合可交换性，难以融入时空或分组等辅助信息。为此，我们提出结构自适应合规q值（SCQ），一种结合个体测试证据与结构模式的显著性指标。同时开发伪分数引导的归纳式自动化模型选择（P-TAMS），使合规化模型选择适配多种候选模型的结构化OOD测试。SCQ与P-TAMS在成对可交换性框架下形成统一系统，提供有限样本误差率控制、更高检验功效及更强可解释性。在模拟与真实数据上的实验表明，该方法能有效控制假发现率，并在多种设置下表现优异。
原文摘要 · Abstract (English)
This paper addresses structured out-of-distribution (OOD) testing in high-stakes machine learning applications. Traditional conformal methods rely on joint exchangeability, making it difficult to incorporate auxiliary information such as spatiotemporal or grouping structures. To overcome this limitation, we propose the structure-adaptive conformal q-value (SCQ), a significance index that integrates individual test evidence with structural patterns. We also develop pseudo-score-guided transductive automated model selection (P-TAMS), which adapts conformalized model selection to structured OOD testing across a toolbox of candidate models. Together, SCQ and P-TAMS form a unified framework under pairwise exchangeability, providing finite-sample error-rate control, improved power, and enhanced interpretability. Experiments on simulated and real data demonstrate that the proposed approach controls the false discovery rate and performs well across diverse settings.
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。