提出无需模型的测试方法，自动判断何时重训及需要多少数据
When to Retrain after Drift: A Data-Only Test of Post-Drift Data Size Sufficiency
- 仅用数据流做单次加权局部回归，通过误差趋势判断数据是否充足
- 在4个领域、3类学习器上表现优于固定数据量重训，且计算开销极低
- 适合实时系统中自动重训场景，尤其关注稳定性和低延迟的部署
突发概念漂移会使先前训练的预测器失效，但何时重训以及重训所需的数据量是否足够却很少被研究。我们提出 CALIPER——一种检测器与模型无关、仅依赖数据的测试方法，用于估计重训所需的后漂移数据量。CALIPER 利用动态系统生成数据流中的状态依赖性：对后漂移窗口进行单次加权局部回归，追踪一步预测误差随局部参数 $θ$ 变化的趋势。当有效样本量门限满足时，若该误差随 $θ$ 增大呈现单调不增趋势，即表明数据量已足够支持稳定重训。我们还提供了理论分析，证明该算法具有低更新时间和内存开销。在四个异构领域、三类学习器和两种漂移检测器的实验中，CALIPER 在保持极低开销的同时，持续达到或超过最佳固定数据量重训效果，常优于增量更新。该方法填补了漂移检测与数据充分适应之间的空白。
原文摘要 · Abstract (English)
Sudden concept drift makes previously trained predictors unreliable, yet deciding when to retrain and what post-drift data size is sufficient is rarely addressed. We propose CALIPER - a detector- and model-agnostic, data-only test that estimates the post-drift data size required for stable retraining. CALIPER exploits state dependence in streams generated by dynamical systems: we run a single-pass weighted local regression over the post-drift window and track a one-step proxy error as a function of a locality parameter $θ$. When an effective sample size gate is satisfied, a monotonically non-increasing trend in this error with increasing a locality parameter indicates that the data size is sufficiently informative for retraining. We also provide a theoretical analysis of our method, and we show that the algorithm has a low per-update time and memory. Across datasets from four heterogeneous domains, three learner families, and two detectors, CALIPER consistently matches or exceeds the best fixed data size for retraining while incurring negligible overhead and often outperforming incremental updates. CALIPER closes the gap between drift detection and data-sufficient adaptation in streaming learning.
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。