English(EN) Ternary Decision Trees with Locally-Adaptive Uncertainty Zones

三元决策树增加不确定性区域以提高准确性

作者 PulseAugur 编辑部 · [3 个来源] · 2026-05-21 04:00

研究人员引入了三元决策树，通过在决策边界周围引入不确定性区域来增强标准二元决策树。该区域允许对子树的预测进行加权混合，并标记不确定的实例以进行不同的下游处理。提出了五种估计不确定性区域宽度的新颖方法并进行了评估，在众多数据集上证明了比传统CART方法显著提高的准确性。 AI

影响引入了一种新颖的决策树方法，通过显式建模决策边界处的不确定性来提高准确性。

排序理由该集群包含两篇详细介绍决策树算法新颖方法的学术论文。

AI 生成摘要 · Google Gemini · 来自 3 个来源。我们如何撰写摘要 →

报道来源 [3]

arXiv cs.LG TIER_1 English(EN) · William Smits · 2026-05-22 04:00

Ternary Decision Trees with Locally-Adaptive Uncertainty Zones

arXiv:2605.22740v1 Announce Type: new Abstract: Decision trees partition the feature space using hard binary thresholds, assigning identical confidence to instances far from a decision boundary and to those directly on it. We introduce ternary decision trees, which augment each s…
arXiv cs.LG TIER_1 English(EN) · William Smits · 2026-05-21 17:11

Ternary Decision Trees with Locally-Adaptive Uncertainty Zones

Decision trees partition the feature space using hard binary thresholds, assigning identical confidence to instances far from a decision boundary and to those directly on it. We introduce ternary decision trees, which augment each split node with an uncertainty zone of half-width…
arXiv stat.ML TIER_1 English(EN) · Martin Bladt, Rasmus Frigaard Lemvig · 2026-05-21 04:00

Consistency of Honest Decision Trees and Random Forests

arXiv:2601.14991v2 Announce Type: replace-cross Abstract: We study various types of consistency of honest decision trees and random forests in the regression setting. In contrast to related literature, our proofs are elementary and follow the classical arguments used for smoothin…