一种可解释的分段线性决策树，通过超平面逐步划分数据提升分类性能。
Learning Hyperplane Tree: A Piecewise Linear and Fully Interpretable Decision-making Framework
- 用超平面逐层划分数据，实现分段线性分类
- 在多个公开数据集上超越现有树模型表现
- 每层决策清晰展示特征贡献，适合需要透明性的场景
本文提出一种新型树结构模型——学习超平面树（Learning Hyperplane Tree, LHT），在多个公开数据集上的分类任务中优于当前最优树模型。LHT结构简洁高效：通过多个超平面逐步划分数据，区分目标类与非目标类样本。尽管单次划分不完全，但通过连续分割有效增强类别区分度。测试时，样本通过评估分支块中的超平面并沿树向下遍历，最终由叶块中基于最小二乘拟合与模糊逻辑构建的分段线性隶属函数确定类别。该模型高度透明可解释，在每个分支块中可清晰观察各特征对分类的贡献。
原文摘要 · Abstract (English)
This paper introduces a novel tree-based model, Learning Hyperplane Tree (LHT), which outperforms state-of-the-art (SOTA) tree models for classification tasks on several public datasets. The structure of LHT is simple and efficient: it partitions the data using several hyperplanes to progressively distinguish between target and non-target class samples. Although the separation is not perfect at each stage, LHT effectively improves the distinction through successive partitions. During testing, a sample is classified by evaluating the hyperplanes defined in the branching blocks and traversing down the tree until it reaches the corresponding leaf block. The class of the test sample is then determined using the piecewise linear membership function defined in the leaf blocks, which is derived through least-squares fitting and fuzzy logic. LHT is highly transparent and interpretable--at each branching block, the contribution of each feature to the classification can be clearly observed.
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