Comparer des méthodes

	Détection d'anomalies par autoencodeur explicable	Isolation Forest Explicable
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2017-2019	2008 / 2017
Auteur d'origine	Combination of autoencoder anomaly detection (Hinton & Salakhutdinov, 2006) and XAI methods (e.g., Lundberg & Lee, 2017)	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (Isolation Forest); Lundberg, S. M. & Lee, S.-I. (SHAP explainability layer)
Type	Unsupervised anomaly detection with post-hoc or intrinsic explainability	Anomaly detection with post-hoc explainability
Source fondatrice	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.
Alias	XAI autoencoder anomaly detection, interpretable autoencoder anomaly detection, explainable deep anomaly detection, SHAP-autoencoder anomaly detection	XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation
Apparentées	6	5
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 Isolation Forest combines the Isolation Forest anomaly detection algorithm with post-hoc explainability tools — most commonly SHAP (SHapley Additive exPlanations) — to not only flag anomalous observations but also reveal which features drove each anomaly score. It bridges unsupervised anomaly detection with the interpretability demands of regulated and high-stakes domains.