So sánh phương pháp

	Isolation Forest Mạnh mẽ	One-Class SVM Mạnh mẽ
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2008–2019	2000s–2010s
Người khởi xướng	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
Loại	Robust ensemble anomaly detection	Anomaly detection / novelty detection
Công trình gốc	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.
Tên gọi khác	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
Liên quan	5	5
Tóm tắt	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.