Bandingkan kaedah

	Hutan Pengasingan Separa Sedia Terkawal	Pembelajaran Separa Selia
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	2013–2020	1970s–2006 (formalized)
Pengasas	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)
Jenis	Ensemble anomaly detection (semi-supervised extension)	Learning paradigm
Sumber perintis	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
Alias	SSIF, semi-supervised iForest, label-guided Isolation Forest, partially supervised Isolation Forest	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Berkaitan	6	5
Ringkasan	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.