مقایسهٔ روش‌ها

	جنگل ایزوله‌سازی مقاوم	ماشین بردار پشتیبان تک کلاسه مقاوم
حوزه	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning
سال پیدایش	2008–2019	2000s–2010s
پدیدآور	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
نوع	Robust ensemble anomaly detection	Anomaly detection / novelty detection
منبع بنیادین	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.
نام‌های دیگر	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
مرتبط	5	5
خلاصه	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.