用预测不确定性提前发现数据分布变化，比误差率更灵敏。
Early Concept Drift Detection via Prediction Uncertainty
- 基于分类器预测不确定性构建新检测指标
- 在误差率不变时仍能识别漂移，检测更早
- 适用于流数据场景，尤其适合图像与结构化数据
概念漂移指流式数据中数据分布随时间不可预测地变化，对机器学习模型构成挑战。尽管基于误差率的漂移检测方法广泛应用，但在数据分布改变而误差率保持稳定时往往无法及时发现漂移。本文提出预测不确定性指数（PU-index），基于分类器的预测不确定性，作为漂移检测的更优替代指标。理论分析表明：(1) 当误差率不变时，PU-index 仍可检测到漂移；(2) 任何误差率的变化都会引起 PU-index 的相应变化。这些特性使 PU-index 比现有方法更敏感、更稳健。我们进一步提出基于 PU-index 的漂移检测器（PUDD），采用新颖的自适应 PU-index 分桶算法进行检测。在合成与真实数据集上的实证评估显示，PUDD 在结构化与图像数据上均具备有效漂移检测能力。
原文摘要 · Abstract (English)
Concept drift, characterized by unpredictable changes in data distribution over time, poses significant challenges to machine learning models in streaming data scenarios. Although error rate-based concept drift detectors are widely used, they often fail to identify drift in the early stages when the data distribution changes but error rates remain constant. This paper introduces the Prediction Uncertainty Index (PU-index), derived from the prediction uncertainty of the classifier, as a superior alternative to the error rate for drift detection. Our theoretical analysis demonstrates that: (1) The PU-index can detect drift even when error rates remain stable. (2) Any change in the error rate will lead to a corresponding change in the PU-index. These properties make the PU-index a more sensitive and robust indicator for drift detection compared to existing methods. We also propose a PU-index-based Drift Detector (PUDD) that employs a novel Adaptive PU-index Bucketing algorithm for detecting drift. Empirical evaluations on both synthetic and real-world datasets demonstrate PUDD's efficacy in detecting drift in structured and image data.
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