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| Self-supervised Isolation Forest× | 지역 이상치 계수 (Local Outlier Factor, LOF)× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2008–2020s | 2000 |
| 창시자≠ | Liu, F. T., Ting, K. M., & Zhou, Z.-H. (iForest); SSL extensions by multiple authors | Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. |
| 유형≠ | Ensemble anomaly detector with self-supervised pre-training | Density-based anomaly 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 ↗ | Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 93–104. DOI ↗ |
| 별칭 | SSL Isolation Forest, self-supervised iForest, semi-supervised isolation forest, contrastive isolation forest | LOF, local outlier factor, density-based outlier detection, local density deviation |
| 관련 | 4 | 4 |
| 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. | Local Outlier Factor (LOF) is a density-based, unsupervised anomaly detection algorithm introduced by Breunig, Kriegel, Ng, and Sander in 2000. It assigns each data point a continuous outlier score that quantifies how isolated that point is relative to its local neighborhood, enabling detection of anomalies that global methods miss because they blend into dense clusters elsewhere in the space. |
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