新算法通过穷举搜索生成更准确、更简洁的决策树
CART-ELC: Oblique Decision Tree Induction via Exhaustive Search
- 在有限超平面中穷举搜索最优切分线
- 小数据集上准确率显著优于传统方法
- 生成的树更浅更简单，适合需要解释性的场景
倾斜决策树因潜在的更高分类性能而受到关注，但依赖穷举搜索寻找倾斜切分的方法面临计算挑战，因而未被广泛探索。本文提出一种新算法CART-ELC，用于诱导倾斜决策树，在受限超平面集合上执行穷举搜索。我们研究了该算法的计算复杂度及其预测能力。结果表明，CART-ELC在小数据集上表现稳定，通常在分类准确率上显著优于现有决策树算法，同时频繁生成更浅、更简单且更具可解释性的树。
原文摘要 · Abstract (English)
Oblique decision trees have attracted attention due to their potential for improved classification performance over traditional axis-aligned decision trees. However, methods that rely on exhaustive search to find oblique splits face computational challenges. As a result, they have not been widely explored. We introduce a novel algorithm, Classification and Regression Tree - Exhaustive Linear Combinations (CART-ELC), for inducing oblique decision trees that performs an exhaustive search on a restricted set of hyperplanes. We then investigate the algorithm's computational complexity and its predictive capabilities. Our results demonstrate that CART-ELC consistently achieves competitive performance on small datasets, often yielding statistically significant improvements in classification accuracy relative to existing decision tree induction algorithms, while frequently producing shallower, simpler, and thus more interpretable trees.
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