方法对比
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| 鲁棒决策树× | 极端随机树 (Extra Trees)× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2000s–2019 | 2006 |
| 提出者≠ | Various (Chen & Nan 2019; robust statistics community) | Geurts, P.; Ernst, D.; Wehenkel, L. |
| 类型≠ | Supervised classification / regression tree | Ensemble (extremely randomized decision trees) |
| 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 ↗ | Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI ↗ |
| 别名 | robust tree, noise-tolerant decision tree, outlier-resistant decision tree, robust CART | Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET |
| 相关≠ | 6 | 5 |
| 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. | Extra Trees (Extremely Randomized Trees), introduced by Geurts, Ernst, and Wehenkel in 2006, is an ensemble of decision trees that pushes randomisation further than Random Forest. Both the candidate features and the split thresholds are chosen completely at random at each node, eliminating the greedy search over thresholds. This extra randomness reduces variance, often matches or exceeds Random Forest accuracy, and runs substantially faster at training time. |
| ScholarGate数据集 ↗ |
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