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| Polu-nadgledani jednoklasni SVM× | Izolacijska šuma× | |
|---|---|---|
| Područje | Strojno učenje | Strojno učenje |
| Obitelj | Machine learning | Machine learning |
| Godina nastanka≠ | 2001–2004 | 2008 |
| Tvorac≠ | Extension of Scholkopf et al. (2001); semi-supervised variants studied ca. 2004–2010 | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Vrsta≠ | Semi-supervised anomaly / novelty detection | Unsupervised ensemble (random partitioning trees) |
| Temeljni izvor≠ | Munoz, A. & Muruzabal, J. (2004). Self-Organising Maps for Outlier Detection. Neurocomputing, 58–60, 953–956. link ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Drugi nazivi≠ | SS-OCSVM, semi-supervised OC-SVM, semi-supervised novelty detection SVM, transductive one-class SVM | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Srodne | 5 | 5 |
| Sažetak≠ | Semi-supervised One-class SVM extends the classic One-class SVM anomaly detector by incorporating unlabeled observations alongside a small set of known normal examples. The unlabeled data helps the model learn a tighter, more informative decision boundary in feature space, reducing false positives and improving anomaly recall compared to the purely unsupervised baseline. | Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets. |
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