Methoden vergelijken

	Semi-supervised Isolation Forest	Isolation Forest
Vakgebied	Machine learning	Machine learning
Familie	Machine learning	Machine learning
Jaar van ontstaan	2013–2020	2008
Grondlegger	Extended from Liu, F.T., Ting, K.M., and Zhou, Z-H. (iForest, 2008); semi-supervised variants developed by multiple authors ca. 2013–2020	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Type	Ensemble anomaly detection (semi-supervised extension)	Unsupervised ensemble (random partitioning trees)
Oorspronkelijke bron	Görnitz, N., Kloft, M., Rieck, K., & Brefeld, U. (2013). Toward supervised anomaly detection. Journal of Artificial Intelligence Research, 46, 235–262.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
Aliassen	SSIF, semi-supervised iForest, label-guided Isolation Forest, partially supervised Isolation Forest	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Verwant	6	5
Samenvatting	Semi-supervised Isolation Forest extends the classic Isolation Forest anomaly detector by incorporating a small set of labeled anomaly (and possibly normal) examples alongside a large unlabeled dataset. This label guidance adjusts the model's anomaly scores so that known anomalies are separated more reliably, bridging the gap between fully unsupervised and fully supervised detection.	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.