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| 説明可能なアイソレーションフォレスト× | オートエンコーダ異常検知× | |
|---|---|---|
| 分野 | 機械学習 | 機械学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 2008 / 2017 | 2006–2014 |
| 提唱者≠ | Liu, F. T., Ting, K. M., & Zhou, Z.-H. (Isolation Forest); Lundberg, S. M. & Lee, S.-I. (SHAP explainability layer) | Hinton, G. E. & Salakhutdinov, R. R. (autoencoders); applied to anomaly detection through multiple authors in the 2010s |
| 種類≠ | Anomaly detection with post-hoc explainability | Unsupervised deep learning (reconstruction-based) |
| 原典≠ | Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link ↗ | Chalapathy, R. & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407. link ↗ |
| 別名 | XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation | AE anomaly detection, reconstruction-error anomaly detection, deep autoencoder outlier detection, unsupervised autoencoder anomaly detection |
| 関連≠ | 5 | 3 |
| 概要≠ | 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. | Autoencoder anomaly detection trains a neural network to compress and then reconstruct normal data. Because the model has only ever learned what normal looks like, anomalous inputs produce noticeably higher reconstruction errors — and those errors become the anomaly score. The method requires no labeled anomalies and scales naturally to high-dimensional data such as sensor streams, images, and log records. |
| ScholarGateデータセット ↗ |
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