方法对比

	鲁棒隔离森林	鲁棒单类支持向量机 (Robust One-Class SVM)
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2008–2019	2000s–2010s
提出者	Liu, F. T., Ting, K. M., Zhou, Z.-H. (base); robust extensions by multiple authors	Extensions of Scholkopf et al. (1999); robust variants developed in 2000s–2010s
类型	Robust ensemble anomaly detection	Anomaly detection / novelty detection
开创性文献	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the IEEE International Conference on Data Mining (ICDM), pp. 413–422. IEEE.	Scholkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., & Platt, J. (1999). Support vector method for novelty detection. Advances in Neural Information Processing Systems (NeurIPS), 12, 582–588.
别名	Robust iForest, noise-robust isolation forest, contamination-robust isolation forest, robust anomaly isolation	Robust OCSVM, Outlier-robust One-Class SVM, Contamination-tolerant OCSVM, Robust novelty detection SVM
相关	5	5
摘要	Robust Isolation Forest extends the classic Isolation Forest anomaly detector with strategies that reduce sensitivity to data contamination, masking effects, and biased random splits. By incorporating robustness mechanisms — such as improved subsampling, re-weighting of suspicious regions, or bias-corrected splitting — it achieves more reliable anomaly scores when the training data itself contains a non-trivial fraction of anomalies or when specific feature distributions cause standard iForest to produce unreliable path lengths.	Robust One-Class SVM extends the classic One-Class Support Vector Machine for novelty and anomaly detection by incorporating robustness mechanisms — such as trimmed objectives, robust kernel choices, or contamination-tolerant loss functions — that reduce the influence of heavy-tailed noise or outliers present in the training data, yielding a decision boundary that better represents the true support of the normal class.