提出流数据回归分析的标准化评估流程，支持多种漂移类型模拟。
Evaluation for Regression Analyses on Evolving Data Streams
- 构建流数据回归评估标准流程，涵盖预测区间任务
- 设计可生成增量漂移等新型漂移的仿真策略
- 验证主流方法在动态数据下的有效性与鲁棒性
本文探讨了流数据中回归分析面临的挑战，该领域相较于分类仍较不成熟。为此，我们提出了流式场景下回归与预测区间任务的标准化评估流程。同时，引入一种创新的漂移模拟策略，能够合成包括较少研究的增量漂移在内的多种漂移类型。通过在先进方法上开展全面实验，验证了所提流程的有效性与鲁棒性。
原文摘要 · Abstract (English)
The paper explores the challenges of regression analysis in evolving data streams, an area that remains relatively underexplored compared to classification. We propose a standardized evaluation process for regression and prediction interval tasks in streaming contexts. Additionally, we introduce an innovative drift simulation strategy capable of synthesizing various drift types, including the less-studied incremental drift. Comprehensive experiments with state-of-the-art methods, conducted under the proposed process, validate the effectiveness and robustness of our approach.
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