Módszerek összehasonlítása

	Bayesian egy-osztályos SVM	Isolation Forest
Tudományterület	Gépi tanulás	Gépi tanulás
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	2001–2010	2008
Megalkotó	Scholkopf et al. (base OCSVM); Bayesian extension via Tipping and others	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Típus	Probabilistic anomaly detection	Unsupervised ensemble (random partitioning trees)
Alapmű	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.
Alternatív nevek	Bayesian OCSVM, Bayesian one-class classifier, probabilistic one-class SVM, Bayes-OCSVM	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Kapcsolódó	6	5
Összefoglaló	Bayesian one-class SVM combines the classical one-class support vector machine — which learns a tight boundary around normal training examples — with Bayesian inference to produce calibrated probability estimates of anomaly, rather than only a binary flag. This allows uncertainty quantification over the novelty decision, making the approach more suitable when downstream actions depend on how confident the model is that a new observation is anomalous.	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.