Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Détection d'anomalies par autoencodeur explicable× | SVM à une classe explicable× | |
|---|---|---|
| Domaine | Apprentissage automatique | Apprentissage automatique |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 2017-2019 | 1999 (OCSVM); 2017–present (explainability integration) |
| Auteur d'origine≠ | Combination of autoencoder anomaly detection (Hinton & Salakhutdinov, 2006) and XAI methods (e.g., Lundberg & Lee, 2017) | Schölkopf, B. et al. (OCSVM); explainability layer via Lundberg & Lee (SHAP, 2017) and related works |
| Type≠ | Unsupervised anomaly detection with post-hoc or intrinsic explainability | Anomaly/novelty detection with post-hoc or intrinsic explainability |
| Source fondatrice≠ | Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. link ↗ | 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 ↗ |
| Alias | XAI autoencoder anomaly detection, interpretable autoencoder anomaly detection, explainable deep anomaly detection, SHAP-autoencoder anomaly detection | XOC-SVM, Interpretable One-Class SVM, SHAP-augmented OCSVM, Explainable Novelty Detection SVM |
| Apparentées≠ | 6 | 4 |
| Résumé≠ | Explainable Autoencoder Anomaly Detection augments a standard autoencoder-based anomaly detector with an interpretability layer — such as SHAP values or feature-wise reconstruction error decomposition — that identifies which input features drove the anomaly flag for each observation, turning an opaque reconstruction-error score into an actionable, human-readable explanation. | 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. |
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