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| 설명 가능한 단일 클래스 SVM (Explainable One-Class SVM)× | Isolation Forest× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1999 (OCSVM); 2017–present (explainability integration) | 2008 |
| 창시자≠ | Schölkopf, B. et al. (OCSVM); explainability layer via Lundberg & Lee (SHAP, 2017) and related works | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| 유형≠ | Anomaly/novelty detection with post-hoc or intrinsic explainability | Unsupervised ensemble (random partitioning trees) |
| 원전≠ | Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., & Platt, J. (1999). Support vector method for novelty detection. Advances in Neural Information Processing Systems, 12, 582–588. link ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| 별칭≠ | XOC-SVM, Interpretable One-Class SVM, SHAP-augmented OCSVM, Explainable Novelty Detection SVM | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| 관련≠ | 4 | 5 |
| 요약≠ | Explainable One-Class SVM pairs the classic One-Class Support Vector Machine anomaly detector — which learns a tight boundary around normal data without requiring labeled anomalies — with post-hoc explainability methods such as SHAP or LIME to reveal which features drive each novelty or anomaly score, converting an opaque decision boundary into an auditable, feature-attributable signal. | 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. |
| ScholarGate데이터셋 ↗ |
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