Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Isolation Forest× | SVM pentru o singură clasă× | |
|---|---|---|
| Domeniu | Învățare automată | Învățare automată |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2008 | 1999–2001 |
| Autorul original≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. | Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C. |
| Tip≠ | Unsupervised ensemble (random partitioning trees) | Anomaly / novelty detection (unsupervised) |
| Sursa seminală≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ | 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. DOI ↗ |
| Denumiri alternative≠ | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection | OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM |
| Înrudite≠ | 5 | 3 |
| Rezumat≠ | 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. | 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. |
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