방법 비교

	능동 학습 Isolation Forest	One-Class SVM
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2008–2019	1999–2001
창시자	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.
유형	Active learning wrapper over isolation forest anomaly detector	Anomaly / novelty detection (unsupervised)
원전	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.
별칭	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
관련	5	3
요약	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.