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| 설명 가능한 단일 클래스 SVM (Explainable One-Class SVM)× | 지역 이상치 계수 (Local Outlier Factor, LOF)× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1999 (OCSVM); 2017–present (explainability integration) | 2000 |
| 창시자≠ | Schölkopf, B. et al. (OCSVM); explainability layer via Lundberg & Lee (SHAP, 2017) and related works | Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. |
| 유형≠ | Anomaly/novelty detection with post-hoc or intrinsic explainability | Density-based anomaly detection (unsupervised) |
| 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 ↗ | 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 | XOC-SVM, Interpretable One-Class SVM, SHAP-augmented OCSVM, Explainable Novelty Detection SVM | LOF, local outlier factor, density-based outlier detection, local density deviation |
| 관련 | 4 | 4 |
| 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. | 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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