动态切换直方图与排序，加速稀疏斜向随机森林训练
Vectorized Adaptive Histograms for Sparse Oblique Forests
- 根据运行时情况自动选择直方图或排序找最优切分点
- 在大规模数据集上训练速度提升1.7至2.5倍
- 支持GPU和混合CPU-GPU部署，适合高性能决策树应用
使用稀疏斜向随机森林进行分类可在控制特定错误类型的同时提供不确定性与置信度保证。然而，由于需要构建深层树并在运行时对数据线性组合进行排序或直方图化，其所需数据量和计算量高于其他树集成方法。本文提出一种动态切换直方图与排序的策略以寻找最佳切分点，并利用向量化指令优化直方图构造。在大型数据集上的评估表明，该优化使训练速度相比现有斜向森林提升1.7-2.5倍，相比标准随机森林提升1.5-2倍。同时提供了GPU及混合CPU-GPU实现。
原文摘要 · Abstract (English)
Classification using sparse oblique random forests provides guarantees on uncertainty and confidence while controlling for specific error types. However, they use more data and more compute than other tree ensembles because they create deep trees and need to sort or histogram linear combinations of data at runtime. We provide a method for dynamically switching between histograms and sorting to find the best split. We further optimize histogram construction using vector intrinsics. Evaluating this on large datasets, our optimizations speedup training by 1.7-2.5x compared to existing oblique forests and 1.5-2x compared to standard random forests. We also provide a GPU and hybrid CPU-GPU implementation.
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