Bandingkan metode

	Isolation Forest Pembelajaran Aktif	SVM Satu Kelas
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	2008–2019	1999–2001
Pencetus	Das, S. et al. (active anomaly discovery framework); Liu, F. T. et al. (Isolation Forest base)	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Tipe	Active learning wrapper over isolation forest anomaly detector	Anomaly / novelty detection (unsupervised)
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.	Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.
Alias	AL-iForest, active anomaly detection with isolation forest, active isolation forest, query-guided isolation forest	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Terkait	5	3
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.	One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available.