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| Self-supervised Isolation Forest× | One-Class SVM× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2008–2020s | 1999–2001 |
| 창시자≠ | Liu, F. T., Ting, K. M., & Zhou, Z.-H. (iForest); SSL extensions by multiple authors | Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C. |
| 유형≠ | Ensemble anomaly detector with self-supervised pre-training | Anomaly / novelty detection (unsupervised) |
| 원전≠ | 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 ↗ | 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 ↗ |
| 별칭 | SSL Isolation Forest, self-supervised iForest, semi-supervised isolation forest, contrastive isolation forest | OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM |
| 관련≠ | 4 | 3 |
| 요약≠ | 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. | 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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