提出一种基于随机超平面的新型贝叶斯树模型，提升预测性能。
Oblique Bayesian additive regression trees
- 用随机超平面替代单特征分割，实现特征组合决策
- 在多个真实与合成数据集上表现优于传统轴对齐树
- 适合需要高精度回归且关注特征交互的场景
现有的贝叶斯加性回归树（BART）基于轴对齐的决策规则，每次仅用一个特征进行递归分割。已有研究证明，基于特征线性组合的斜向树（oblique trees）在某些情况下可提供更优预测效果，并具备良好理论性质。本文提出一种斜向BART方法，采用数据自适应的决策规则先验，通过随机超平面递归划分特征空间。在多个合成与真实世界基准数据集上，系统对比了该方法与轴对齐BART及其他树集成方法，结果表明斜向BART表现具有竞争力，有时显著更优。
原文摘要 · Abstract (English)
Current implementations of Bayesian Additive Regression Trees (BART) are based on axis-aligned decision rules that recursively partition the feature space using a single feature at a time. Several authors have demonstrated that oblique trees, whose decision rules are based on linear combinations of features, can sometimes yield better predictions than axis-aligned trees and exhibit excellent theoretical properties. We develop an oblique version of BART that leverages a data-adaptive decision rule prior that recursively partitions the feature space along random hyperplanes. Using several synthetic and real-world benchmark datasets, we systematically compared our oblique BART implementation to axis-aligned BART and other tree ensemble methods, finding that oblique BART was competitive with -- and sometimes much better than -- those methods.
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