Compară metode

	Online Isolation Forest	SVM pentru o singură clasă
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2008–2011	1999–2001
Autorul original	Tan, S. C.; Ting, K. M.; Liu, T. F. (streaming variant); original iForest by Liu et al.	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Tip	Streaming anomaly detection (online ensemble)	Anomaly / novelty detection (unsupervised)
Sursa seminală	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 413–422.	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.
Denumiri alternative	streaming isolation forest, incremental isolation forest, online iForest, adaptive isolation forest	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Înrudite	6	3
Rezumat	Online Isolation Forest extends the Isolation Forest anomaly-detection algorithm to streaming or continuously arriving data. Instead of rebuilding isolation trees from scratch when new observations arrive, the forest is updated incrementally so that anomaly scores remain current without reprocessing the entire history. This makes it practical for real-time monitoring, fraud detection, and sensor-data surveillance where data volumes grow indefinitely.	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.