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| 앙상블 아이솔레이션 포레스트× | One-Class SVM× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2008 (base); ensemble variants 2010s–present | 1999–2001 |
| 창시자≠ | Liu, F. T., Ting, K. M., Zhou, Z.-H. (base IF); ensemble extensions by multiple researchers | Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C. |
| 유형≠ | Meta-ensemble anomaly detection | 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 2008), pp. 413–422. IEEE. 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 | EIF ensemble, multi-isolation-forest, isolation forest ensemble, ensemble anomaly detection with isolation trees | OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM |
| 관련≠ | 5 | 3 |
| 요약≠ | Ensemble Isolation Forest trains multiple Isolation Forest models — each with different random seeds, subsampling ratios, or contamination parameters — and combines their anomaly scores to produce a more stable, robust anomaly ranking. By averaging or aggregating across several independent isolation forests, the method reduces the variance inherent in any single forest and yields more reliable outlier detection on complex or high-dimensional 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. |
| ScholarGate데이터셋 ↗ |
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