Сравнение методов

	Устойчивый Isolation Forest	Одноклассовая SVM
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2008–2019	1999–2001
Автор метода	Liu, F. T., Ting, K. M., Zhou, Z.-H. (base); robust extensions by multiple authors	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Тип	Robust ensemble anomaly detection	Anomaly / novelty detection (unsupervised)
Основополагающий источник	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., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.
Другие названия	Robust iForest, noise-robust isolation forest, contamination-robust isolation forest, robust anomaly isolation	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Связанные	5	3
Сводка	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.	One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available.