Salīdzināt metodes

	Pusuzraudzītā izolācijas meža metode (Semi-supervised Isolation Forest)	Daudzpusīgā apguve
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2013–2020	1970s–2006 (formalized)
Autors	Extended from Liu, F.T., Ting, K.M., and Zhou, Z-H. (iForest, 2008); semi-supervised variants developed by multiple authors ca. 2013–2020	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Tips	Ensemble anomaly detection (semi-supervised extension)	Learning paradigm
Pirmavots	Görnitz, N., Kloft, M., Rieck, K., & Brefeld, U. (2013). Toward supervised anomaly detection. Journal of Artificial Intelligence Research, 46, 235–262.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Citi nosaukumi	SSIF, semi-supervised iForest, label-guided Isolation Forest, partially supervised Isolation Forest	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Saistītās	6	5
Kopsavilkums	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.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.