Bandingkan metode

	Semi-supervised One-class SVM	Isolation Forest
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	2001–2004	2008
Pencetus	Extension of Scholkopf et al. (2001); semi-supervised variants studied ca. 2004–2010	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Tipe	Semi-supervised anomaly / novelty detection	Unsupervised ensemble (random partitioning trees)
Sumber perintis	Munoz, A. & Muruzabal, J. (2004). Self-Organising Maps for Outlier Detection. Neurocomputing, 58–60, 953–956.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
Alias	SS-OCSVM, semi-supervised OC-SVM, semi-supervised novelty detection SVM, transductive one-class SVM	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Terkait	5	5
Ringkasan	Semi-supervised One-class SVM extends the classic One-class SVM anomaly detector by incorporating unlabeled observations alongside a small set of known normal examples. The unlabeled data helps the model learn a tighter, more informative decision boundary in feature space, reducing false positives and improving anomaly recall compared to the purely unsupervised baseline.	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.