方法对比
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| 鲁棒决策树× | 正则化决策树× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2000s–2019 | 1984 |
| 提出者≠ | Various (Chen & Nan 2019; robust statistics community) | Breiman, L., Friedman, J., Olshen, R., & Stone, C. |
| 类型≠ | Supervised classification / regression tree | Supervised learning (regularized tree) |
| 开创性文献≠ | Chen, H., & Nan, F. (2019). Robust Decision Trees Against Adversarial Examples. Proceedings of the 36th International Conference on Machine Learning (ICML), PMLR 97, 1006–1015. link ↗ | Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8 |
| 别名 | robust tree, noise-tolerant decision tree, outlier-resistant decision tree, robust CART | pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART |
| 相关 | 6 | 6 |
| 摘要≠ | A Robust Decision Tree is a decision tree variant trained with modified splitting criteria or training procedures designed to reduce sensitivity to outliers, label noise, and adversarial perturbations. Rather than minimizing standard impurity measures that are strongly affected by extreme values, robust variants use statistically robust analogues or regularization to produce splits that generalize under noisy or corrupted data conditions. | A regularized decision tree is a decision tree model whose complexity is intentionally limited through pruning, depth constraints, or penalty terms to prevent overfitting. Rooted in Breiman et al.'s CART framework (1984), regularization converts the greedy tree-growing procedure into a bias-variance tradeoff, yielding models that generalize better to unseen data than fully-grown trees. |
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