改进决策树在不平衡连续数据流中的预测效果
On Imbalanced Regression with Hoeffding Trees
- 用望远镜式KDE提升流数据早期预测精度
- 分层收缩法对增量决策树增益有限
- 适合处理实时、分布不均的回归任务
许多现实应用产生连续数据流用于回归分析。霍夫丁树及其变体因高效性而长期被使用，无论是独立应用还是作为更广泛集成模型的基础。近期批处理学习研究显示，核密度估计（KDE）可改善不平衡回归中的平滑预测 [Yang et al., 2021]，而分层收缩（HS）则可在不修改树结构的前提下提供事后正则化 [Agarwal et al., 2022]。本文通过望远镜式公式将KDE扩展至流式设置，并将HS整合进增量决策树中。在标准在线回归基准上的实证评估表明，KDE在早期流阶段表现稳定提升，而HS带来的增益有限。代码已公开：https://github.com/marinaAlchirch/DSFA_2026。
原文摘要 · Abstract (English)
Many real-world applications generate continuous data streams for regression. Hoeffding trees and their variants have a long-standing tradition due to their effectiveness, either alone or as base models in broader ensembles. Recent batch-learning work shows that kernel density estimation (KDE) improves smoothed predictions in imbalanced regression [Yang et al., 2021], while hierarchical shrinkage (HS) provides post-hoc regularization for decision trees without modifying their structure [Agarwal et al., 2022]. We extend KDE to streaming settings via a telescoping formulation and integrate HS into incremental decision trees. Empirical evaluation on standard online regression benchmarks shows that KDE consistently improves early-stream performance, whereas HS provides limited gains. Our implementation is publicly available at: https://github.com/marinaAlchirch/DSFA_2026.
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