改进决策树离散化方法，让模型在偏态数据上更准更快。
Adaptive MSD-Splitting: Enhancing C4.5 and Random Forests for Skewed Continuous Attributes
- 根据特征偏度动态调整分箱标准差倍数，提升精度。
- 在多个真实数据集上比原方法高2-4%准确率，速度不变。
- 适合处理生物医学、金融等偏态数据的大型集成学习任务。
连续属性的离散化仍是决策树构建中的计算瓶颈，尤其在高维数据下更为突出。基于近期提出的MSD-Splitting技术（利用经验均值和标准差分箱，显著提升C4.5算法效率与精度），本文提出自适应MSD-Splitting（AMSD）。标准MSD-Splitting对近似对称分布有效，但在高度偏态数据中因固定1倍标准差的切割阈值，易导致信息丢失——这在真实世界中的生物医学与金融数据中常见。AMSD通过根据特征偏度动态调整标准差乘数，在密集区域缩小区间以保留判别能力。此外，我们将AMSD整合进集成方法，提出随机森林-自适应分箱（RF-AMSD）框架。在Census Income、Heart Disease、Breast Cancer和Forest Covertype数据集上的实证评估表明，AMSD相较标准MSD-Splitting实现2%-4%的准确率提升，同时保持与原始方法相当的近O(N)时间复杂度，远优于传统的O(N log N)穷举搜索。所提随机森林扩展在极低计算成本下达到当前最优精度，验证了自适应统计分箱在大规模集成学习中的可行性。
原文摘要 · Abstract (English)
The discretization of continuous numerical attributes remains a persistent computational bottleneck in the induction of decision trees, particularly as dataset dimensions scale. Building upon the recently proposed MSD-Splitting technique -- which bins continuous data using the empirical mean and standard deviation to dramatically improve the efficiency and accuracy of the C4.5 algorithm -- we introduce Adaptive MSD-Splitting (AMSD). While standard MSD-Splitting is highly effective for approximately symmetric distributions, its rigid adherence to fixed one-standard-deviation cutoffs can lead to catastrophic information loss in highly skewed data, a common artifact in real-world biomedical and financial datasets. AMSD addresses this by dynamically adjusting the standard deviation multiplier based on feature skewness, narrowing intervals in dense regions to preserve discriminative resolution. Furthermore, we integrate AMSD into ensemble methods, specifically presenting the Random Forest-AMSD (RF-AMSD) framework. Empirical evaluations on the Census Income, Heart Disease, Breast Cancer, and Forest Covertype datasets demonstrate that AMSD yields a 2-4% accuracy improvement over standard MSD-Splitting, while maintaining near-identical O(N) time complexity reductions compared to the O(N log N) exhaustive search. Our Random Forest extension achieves state-of-the-art accuracy at a fraction of standard computational costs, confirming the viability of adaptive statistical binning in large-scale ensemble learning architectures.
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。