Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Aktiivinen oppiminen – yhden luokan SVM× | Isolation Forest× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2000s | 2008 |
| Kehittäjä≠ | Schölkopf et al. (OCSVM); active variant developed in the anomaly-detection literature (2000s–2010s) | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Tyyppi≠ | Semi-supervised anomaly/novelty detection with iterative labeling | Unsupervised ensemble (random partitioning trees) |
| Alkuperäislähde≠ | Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (1999). Estimating the Support of a High-Dimensional Distribution. Neural Computation, 13(7), 1443–1471. DOI ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Rinnakkaisnimet≠ | AL-OCSVM, active one-class SVM, active novelty detection SVM, query-driven OCSVM | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Liittyvät≠ | 4 | 5 |
| Tiivistelmä≠ | Active Learning One-class SVM combines the one-class support vector machine — a kernel-based novelty detector that learns the boundary of normal data — with an active learning loop that selects the most informative unlabeled instances for expert annotation. The result is a data-efficient anomaly detector that improves its decision boundary with minimal labeling effort. | 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. |
| ScholarGateAineisto ↗ |
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