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| Forklarlig Isolation Forest× | Forklarbare K-Nærmeste Naboer× | |
|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2008 / 2017 | 1967 (KNN); 2010s (explainability extensions) |
| Ophavsperson≠ | Liu, F. T., Ting, K. M., & Zhou, Z.-H. (Isolation Forest); Lundberg, S. M. & Lee, S.-I. (SHAP explainability layer) | Cover, T. & Hart, P. (KNN); XAI extensions by various authors |
| Type≠ | Anomaly detection with post-hoc explainability | Instance-based learning with explainability layer |
| Oprindelig kilde≠ | Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link ↗ | Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. DOI ↗ |
| Aliasser | XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation | XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors |
| Relaterede≠ | 5 | 4 |
| Resumé≠ | 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. | Explainable K-Nearest Neighbors (XKNN) augments the classic KNN classifier or regressor with structured post-hoc or built-in explanation mechanisms, exposing which retrieved neighbors, which features, and which distance contributions drive each individual prediction — making the model's reasoning transparent and auditable for human decision-makers. |
| ScholarGateDatasæt ↗ |
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