เปรียบเทียบวิธี

	Explainable One-Class SVM	Isolation Forest
สาขาวิชา	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	1999 (OCSVM); 2017–present (explainability integration)	2008
ผู้ริเริ่ม	Schölkopf, B. et al. (OCSVM); explainability layer via Lundberg & Lee (SHAP, 2017) and related works	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
ประเภท	Anomaly/novelty detection with post-hoc or intrinsic explainability	Unsupervised ensemble (random partitioning trees)
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	XOC-SVM, Interpretable One-Class SVM, SHAP-augmented OCSVM, Explainable Novelty Detection SVM	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
ที่เกี่ยวข้อง	4	5
สรุป	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.