Bandingkan metode

	Isolation Forest Pembelajaran Aktif	Semi-supervised Isolation Forest
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	2008–2019	2013–2020
Pencetus	Das, S. et al. (active anomaly discovery framework); Liu, F. T. et al. (Isolation Forest base)	Extended from Liu, F.T., Ting, K.M., and Zhou, Z-H. (iForest, 2008); semi-supervised variants developed by multiple authors ca. 2013–2020
Tipe	Active learning wrapper over isolation forest anomaly detector	Ensemble anomaly detection (semi-supervised extension)
Sumber perintis	Das, S., Wong, W. K., Fern, A., Dietterich, T. G., & Amran Siddiqui, M. (2019). Incorporating Expert Feedback into Active Anomaly Discovery. In Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM), pp. 1009–1014.	Görnitz, N., Kloft, M., Rieck, K., & Brefeld, U. (2013). Toward supervised anomaly detection. Journal of Artificial Intelligence Research, 46, 235–262.
Alias	AL-iForest, active anomaly detection with isolation forest, active isolation forest, query-guided isolation forest	SSIF, semi-supervised iForest, label-guided Isolation Forest, partially supervised Isolation Forest
Terkait	5	6
Ringkasan	Active Learning Isolation Forest combines the unsupervised anomaly-scoring power of Isolation Forest with an iterative query strategy that asks a human expert to label the most informative instances. The result is a detector that refines its anomaly boundaries using a minimal labeling budget, dramatically improving precision on rare and subtle anomalies compared to a purely unsupervised baseline.	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.