방법 비교

	강건 단일 클래스 SVM	강건한 Isolation Forest
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2000s–2010s	2008–2019
창시자	Extensions of Scholkopf et al. (1999); robust variants developed in 2000s–2010s	Liu, F. T., Ting, K. M., Zhou, Z.-H. (base); robust extensions by multiple authors
유형	Anomaly detection / novelty detection	Robust ensemble anomaly detection
원전	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.	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.
별칭	Robust OCSVM, Outlier-robust One-Class SVM, Contamination-tolerant OCSVM, Robust novelty detection SVM	Robust iForest, noise-robust isolation forest, contamination-robust isolation forest, robust anomaly isolation
관련	5	5
요약	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.	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.