通过特征拼接提升斜决策树学习效率，让模型更省参数、更准
Enhance Learning Efficiency of Oblique Decision Tree via Feature Concatenation
- 用特征拼接让斜决策树的线性投影能传递到子节点
- 浅层树结构下比现有方法准确率更高，泛化能力更强
- 适合追求高效低耗的决策树应用，如嵌入式系统
斜决策树（ODT）通过线性投影分割特征空间，相比传统轴平行分裂的决策树具有更强表达能力，可构建更浅的树结构并逼近复杂决策边界。然而其学习效率仍不足，因线性投影无法传递至子节点，造成参数浪费。本文提出基于特征拼接的增强型斜决策树（FC-ODT），实现模型内特征变换，使投影信息沿决策路径传递。理论上证明该方法在树深度上具更快一致性收敛速率，表明其在浅层树中具备显著泛化优势。实验显示，FC-ODT在有限树深下优于其他先进决策树方法。
原文摘要 · Abstract (English)
Oblique Decision Tree (ODT) separates the feature space by linear projections, as opposed to the conventional Decision Tree (DT) that forces axis-parallel splits. ODT has been proven to have a stronger representation ability than DT, as it provides a way to create shallower tree structures while still approximating complex decision boundaries. However, its learning efficiency is still insufficient, since the linear projections cannot be transmitted to the child nodes, resulting in a waste of model parameters. In this work, we propose an enhanced ODT method with Feature Concatenation (\texttt{FC-ODT}), which enables in-model feature transformation to transmit the projections along the decision paths. Theoretically, we prove that our method enjoys a faster consistency rate w.r.t. the tree depth, indicating that our method possesses a significant advantage in generalization performance, especially for shallow trees. Experiments show that \texttt{FC-ODT} can outperform the other state-of-the-art decision trees with a limited tree depth.
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