Võrdle meetodeid

	Ensemble Isolation Forest	Üheklassi SVM
Valdkond	Masinõpe	Masinõpe
Perekond	Machine learning	Machine learning
Tekkeaasta	2008 (base); ensemble variants 2010s–present	1999–2001
Looja	Liu, F. T., Ting, K. M., Zhou, Z.-H. (base IF); ensemble extensions by multiple researchers	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Tüüp	Meta-ensemble anomaly detection	Anomaly / novelty detection (unsupervised)
Algallikas	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), pp. 413–422. IEEE.	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.
Rööpnimetused	EIF ensemble, multi-isolation-forest, isolation forest ensemble, ensemble anomaly detection with isolation trees	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Seotud	5	3
Kokkuvõte	Ensemble Isolation Forest trains multiple Isolation Forest models — each with different random seeds, subsampling ratios, or contamination parameters — and combines their anomaly scores to produce a more stable, robust anomaly ranking. By averaging or aggregating across several independent isolation forests, the method reduces the variance inherent in any single forest and yields more reliable outlier detection on complex or high-dimensional data.	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.