Сравнение методов

	Объяснимый одноклассовый SVM	Локальный фактор выбросов (Local Outlier Factor, LOF)
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1999 (OCSVM); 2017–present (explainability integration)	2000
Автор метода	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.
Тип	Anomaly/novelty detection with post-hoc or intrinsic explainability	Density-based anomaly detection (unsupervised)
Основополагающий источник	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.	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.
Другие названия	XOC-SVM, Interpretable One-Class SVM, SHAP-augmented OCSVM, Explainable Novelty Detection SVM	LOF, local outlier factor, density-based outlier detection, local density deviation
Связанные	4	4
Сводка	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.