提出可迁移的流数据概念漂移检测方法，提升动态环境下的优化适应性。
TRACE: A Generalizable Drift Detector for Streaming Data-Driven Optimization
- 通过分词策略提取统计特征，用注意力机制学习漂移模式。
- 在未见过的数据集上实现高精度漂移检测，验证了模式可迁移性。
- 可直接嵌入流优化器，适合复杂动态环境中的自适应优化场景。
许多优化任务涉及未知概念漂移的流数据，构成流数据驱动优化（SDDO）的重大挑战。现有方法虽利用代理模型近似和历史知识迁移，但常受限于固定漂移间隔和完全环境可观测等假设，难以适应多样化的动态环境。我们提出TRACE（TRAnsferable Concept-drift Estimator），一种能有效检测不同时间尺度下流数据分布变化的方法。TRACE采用原理性的分词策略从数据流中提取统计特征，并通过基于注意力的序列学习建模漂移模式，实现对未见数据集的准确检测，凸显所学漂移模式的可迁移性。此外，我们通过将TRACE集成至流优化器，展示了其即插即用特性，支持在未知漂移下实现自适应优化。在多种基准上的全面实验表明，该方法在SDDO场景中具有优异的泛化性、鲁棒性和有效性。
原文摘要 · Abstract (English)
Many optimization tasks involve streaming data with unknown concept drifts, posing a significant challenge as Streaming Data-Driven Optimization (SDDO). Existing methods, while leveraging surrogate model approximation and historical knowledge transfer, are often under restrictive assumptions such as fixed drift intervals and fully environmental observability, limiting their adaptability to diverse dynamic environments. We propose TRACE, a TRAnsferable C}oncept-drift Estimator that effectively detects distributional changes in streaming data with varying time scales. TRACE leverages a principled tokenization strategy to extract statistical features from data streams and models drift patterns using attention-based sequence learning, enabling accurate detection on unseen datasets and highlighting the transferability of learned drift patterns. Further, we showcase TRACE's plug-and-play nature by integrating it into a streaming optimizer, facilitating adaptive optimization under unknown drifts. Comprehensive experimental results on diverse benchmarks demonstrate the superior generalization, robustness, and effectiveness of our approach in SDDO scenarios.
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