English(EN) Scaling Optimal Classification Trees via Adaptive Feature and Sample Reduction

新框架通过自适应缩减来扩展最优分类树

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一个新框架，通过自适应地缩减特征和样本空间来扩展最优分类树。该方法基于 STreeD，使用加权表示来合并重复记录，并迭代地优化候选特征集。实验表明，与标准的 STreeD 相比，速度显著提升，同时 Adaptive STreeD 在处理其他方法难以应对的大型数据集和深度时，保持了可比的预测性能。 AI

影响提高了基于树的模型的可扩展性，有可能在更大的数据集上进行更复杂的分析。

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arXiv cs.LG TIER_1 English(EN) · Jiancheng Tu, Wenqi Fan · 2026-09-10 04:00

通过自适应特征和样本缩减实现最优分类树的扩展

arXiv:2609.05826v1 Announce Type: new Abstract: Dynamic programming for optimal classification trees becomes computationally expensive as the numbers of features and training samples increase. We develop a joint feature- and sample-space reduction framework based on STreeD. Weigh…

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通过自适应特征和样本缩减实现最优分类树的扩展

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