方法对比
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| 鲁棒随机森林× | 孤立森林 (Isolation Forest)× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2000s–2010s | 2008 |
| 提出者≠ | Various (extensions of Breiman 2001 Random Forest) | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| 类型≠ | Robust Ensemble (noise-tolerant bagging of decision trees) | Unsupervised ensemble (random partitioning trees) |
| 开创性文献≠ | Chen, S., & Guestrin, C. (2019). Robust Random Forest. In Proceedings of the 36th International Conference on Machine Learning (ICML). Also see: Gao, W., & Zhou, Z.-H. (2013). On the Doubt about Margin Explanation of Boosting. Artificial Intelligence, 203, 1–18. link ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| 别名≠ | RRF, noise-robust random forest, outlier-resistant random forest, robust ensemble forest | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| 相关≠ | 6 | 5 |
| 摘要≠ | Robust Random Forest extends the standard Random Forest ensemble by incorporating mechanisms that reduce the influence of outliers, label noise, and corrupted observations. Rather than treating all training instances equally, it applies weighting or filtering strategies so that noisy or anomalous samples contribute less to individual tree splits, yielding predictions that remain reliable even when data quality is imperfect. | Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets. |
| ScholarGate数据集 ↗ |
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