Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Self-supervised Isolation Forest× | Isolation Forest× | |
|---|---|---|
| Domeniu | Învățare automată | Învățare automată |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2008–2020s | 2008 |
| Autorul original≠ | Liu, F. T., Ting, K. M., & Zhou, Z.-H. (iForest); SSL extensions by multiple authors | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Tip≠ | Ensemble anomaly detector with self-supervised pre-training | Unsupervised ensemble (random partitioning trees) |
| Sursa seminală≠ | Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 413–422. DOI ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Denumiri alternative≠ | SSL Isolation Forest, self-supervised iForest, semi-supervised isolation forest, contrastive isolation forest | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Înrudite≠ | 4 | 5 |
| Rezumat≠ | Self-supervised Isolation Forest augments the classic Isolation Forest anomaly detector with a self-supervised pre-training stage. A pretext task — such as predicting rotation, masked features, or contrastive pairs — is solved without labels to learn a richer feature representation, which is then used when building the isolation trees, yielding sharper anomaly scores on complex, high-dimensional tabular data. | 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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