发表机构
Pusan National University（釜山大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出带自适应剪枝的多分岔神经决策树（MBNDT），在21个OpenML二分类基准的受深度约束单树中获最佳平均排名与平衡准确率，多分支分裂是性能提升关键。
AI 中文摘要
决策树在表格预测任务中颇具吸引力，因为每次预测都遵循可解释的特征阈值测试序列。然而，在严格的最大深度预算下，传统二叉树可能表达能力不足，因为每个内部节点仅做出单一阈值决策。我们研究浅层树归纳，目标是在保持根到叶路径较短的同时提高准确率。我们提出了带自适应剪枝的多分岔神经决策树（MBNDT），这是一种单轴对齐树，通过可微分多分支分裂进行端到端训练。每个内部节点学习所选特征上的有序阈值和分支掩码，以调整其有效元数，训练后的模型会转换为确定性单路径树用于推理。在21个OpenML二分类基准测试中，MBNDT在受深度约束的单树基线中取得了最佳平均排名和平均平衡准确率；受控 ablation 实验将性能提升归因于多分支分裂。这些提升存在明确权衡：MBNDT比其他单树基线实现了更多叶子节点，因此最适合优先考虑短且有界决策路径下的准确率而非最小全局树规模的场景。
英文摘要
Decision trees are attractive for tabular prediction tasks because each prediction follows an interpretable sequence of feature-threshold tests. Under a strict maximum-depth budget, however, conventional binary trees can be under-expressive, since each internal node makes only a single threshold decision. We study shallow-depth tree induction, where the goal is to improve accuracy while keeping root-to-leaf paths short. We propose the Multi-Branch Neural Decision Tree with Adaptive Pruning (MBNDT), a single axis-aligned tree trained end-to-end with differentiable multi-way splits. Each internal node learns ordered thresholds over a selected feature and a branch mask that adapts its effective arity, and the trained model is converted to a deterministic single-path tree for inference. Across 21 OpenML binary-classification benchmarks, MBNDT achieves the best average rank and mean balanced accuracy among depth-constrained single-tree baselines; a controlled ablation isolates multi-way splitting as the source of the gain. These gains come with an explicit trade-off: MBNDT realizes more leaves than the other single-tree baselines, making it best suited when accuracy under short, bounded decision paths is prioritized over minimal global tree size.
Comments9 pages, 1 pages for the appendix
