提出基于直方图的采样方法，提升数据流中罕见值的回归预测精度。
Histogram Approaches for Imbalanced Data Streams Regression
- 用动态直方图识别分布任意区域的稀有样本并优先处理。
- 在真实与合成数据上，稀有情况预测误差降低显著，优于基线模型。
- 适用于持续变化的数据流场景，适合工业实时预测需求。
不平衡数据域在现实世界预测分析中构成重大挑战，尤其在回归任务中。尽管现有研究多聚焦于静态数据集的批量学习，针对在线学习场景下的不平衡回归关注有限。此前工作提出基于切比雪夫不等式的采样策略，是首个专为数据流设计的方法，但受限于仅将罕见实例假设位于分布极值区。本研究提出基于直方图的采样策略，突破该限制，引入增量式在线直方图以动态检测并优先处理目标分布任意区域的稀有实例，提升罕见情形下的预测性能。所提方法包括直方图下采样（HistUS）和直方图上采样（HistOS），在合成与真实世界基准数据集上的全面实验表明，两者显著改善了稀有案例预测准确率，优于基线模型，且与切比雪夫方法竞争力相当。
原文摘要 · Abstract (English)
Imbalanced domains pose a significant challenge in real-world predictive analytics, particularly in the context of regression. While existing research has primarily focused on batch learning from static datasets, limited attention has been given to imbalanced regression in online learning scenarios. Intending to address this gap, in prior work, we proposed sampling strategies based on Chebyshevs inequality as the first methodologies designed explicitly for data streams. However, these approaches operated under the restrictive assumption that rare instances exclusively reside at distribution extremes. This study introduces histogram-based sampling strategies to overcome this constraint, proposing flexible solutions for imbalanced regression in evolving data streams. The proposed techniques -- Histogram-based Undersampling (HistUS) and Histogram-based Oversampling (HistOS) -- employ incremental online histograms to dynamically detect and prioritize rare instances across arbitrary regions of the target distribution to improve predictions in the rare cases. Comprehensive experiments on synthetic and real-world benchmarks demonstrate that HistUS and HistOS substantially improve rare-case prediction accuracy, outperforming baseline models while maintaining competitiveness with Chebyshev-based approaches.
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