เปรียบเทียบวิธี

	Active Learning Isolation Forest	One-class SVM
สาขาวิชา	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2008–2019	1999–2001
ผู้ริเริ่ม	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.
ประเภท	Active learning wrapper over isolation forest anomaly detector	Anomaly / novelty detection (unsupervised)
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	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
ที่เกี่ยวข้อง	5	3
สรุป	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.