Σύγκριση μεθόδων

	One-Class SVM	Isolation Forest
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1999–2001	2008
Δημιουργός	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Τύπος	Anomaly / novelty detection (unsupervised)	Unsupervised ensemble (random partitioning trees)
Θεμελιώδης πηγή	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.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
Εναλλακτικές ονομασίες	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Συναφείς	3	5
Σύνοψη	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.	Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets.