Linganisha mbinu

	Isolation Forest Imara	One-Class SVM
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2008–2019	1999–2001
Mwanzilishi	Liu, F. T., Ting, K. M., Zhou, Z.-H. (base); robust extensions by multiple authors	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Aina	Robust ensemble anomaly detection	Anomaly / novelty detection (unsupervised)
Chanzo asilia	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the IEEE International Conference on Data Mining (ICDM), 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.
Majina mbadala	Robust iForest, noise-robust isolation forest, contamination-robust isolation forest, robust anomaly isolation	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Zinazohusiana	5	3
Muhtasari	Robust Isolation Forest extends the classic Isolation Forest anomaly detector with strategies that reduce sensitivity to data contamination, masking effects, and biased random splits. By incorporating robustness mechanisms — such as improved subsampling, re-weighting of suspicious regions, or bias-corrected splitting — it achieves more reliable anomaly scores when the training data itself contains a non-trivial fraction of anomalies or when specific feature distributions cause standard iForest to produce unreliable path lengths.	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.