قارن الطرق

	آلة المتجهات الداعمة أحادية الفئة القابلة للتفسير	غابة العزل
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	1999 (OCSVM); 2017–present (explainability integration)	2008
صاحب الطريقة	Schölkopf, B. et al. (OCSVM); explainability layer via Lundberg & Lee (SHAP, 2017) and related works	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
النوع	Anomaly/novelty detection with post-hoc or intrinsic explainability	Unsupervised ensemble (random partitioning trees)
المصدر التأسيسي	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.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
الأسماء البديلة	XOC-SVM, Interpretable One-Class SVM, SHAP-augmented OCSVM, Explainable Novelty Detection SVM	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
ذات صلة	4	5
الملخص	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.