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半监督决策树

半监督决策树是对标准决策树归纳（如 CART 或 C4.5）的扩展，它利用未标记的观测数据以及标记的训练集。通过迭代地为未标记数据分配暂定标签并将其纳入生长或分裂过程，该算法可以实现比仅在标记子集上训练的全监督树更高的准确性，这在标记成本高昂或耗时时尤其有价值。

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Method map

The neighbourhood of related methods — select a node to explore.

半监督决策树
决策树梯度提升（Gradient Boosting）标签传播随机森林主动学习决策树在线决策树

来源

  1. Levin, E. & Shapiro, E. (2000). Learning Decision Trees from Semi-labeled Examples. Proceedings of the ICML Workshop on Attribute-Value and Relational Learning. link ↗
  2. Zhu, X. & Goldberg, A. B. (2009). Introduction to Semi-Supervised Learning. Morgan & Claypool Publishers. ISBN: 978-1-598-29548-9

如何引用本页

ScholarGate. (2026, June 3). Semi-supervised Decision Tree Learning. ScholarGate. https://scholargate.app/zh/machine-learning/semi-supervised-decision-tree

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Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

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主动学习决策树在线决策树

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ScholarGate — Semi-supervised Decision Tree (Semi-supervised Decision Tree Learning). 于 2026-06-15 检索自 https://scholargate.app/zh/machine-learning/semi-supervised-decision-tree · 数据集： https://doi.org/10.5281/zenodo.20539026