在标签稀缺下高效检测局部概念漂移
An Adaptive Sampling Framework for Detecting Localized Concept Drift under Label Scarcity
- 基于残差的自适应采样，动态平衡探索与利用
- 在有限标注预算下，检测准确率显著提升
- 适合工业场景中数据标注成本高的回归任务
概念漂移与标签稀缺是动态工业环境中制约预测模型鲁棒性的两大关键挑战。现有漂移检测方法通常假设全局漂移且依赖密集监督，难以适用于具有局部漂移和标签有限的回归任务。本文提出一种自适应采样框架，结合基于残差的探索与利用策略及EWMA监控机制，在标注预算约束下高效检测局部概念漂移。在合成基准和电力市场案例研究中，该方法展现出更优的标签使用效率与漂移检测精度。
原文摘要 · Abstract (English)
Concept drift and label scarcity are two critical challenges limiting the robustness of predictive models in dynamic industrial environments. Existing drift detection methods often assume global shifts and rely on dense supervision, making them ill-suited for regression tasks with local drifts and limited labels. This paper proposes an adaptive sampling framework that combines residual-based exploration and exploitation with EWMA monitoring to efficiently detect local concept drift under labeling budget constraints. Empirical results on synthetic benchmarks and a case study on electricity market demonstrate superior performance in label efficiency and drift detection accuracy.
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