Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Förklaringsbar isolationsskogar× | Förklaringsbar K närmaste grannar× | |
|---|---|---|
| Ämnesområde | Maskininlärning | Maskininlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 2008 / 2017 | 1967 (KNN); 2010s (explainability extensions) |
| Upphovsperson≠ | 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 |
| Typ≠ | Anomaly detection with post-hoc explainability | Instance-based learning with explainability layer |
| Ursprungskälla≠ | 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 ↗ |
| Alias | XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation | XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors |
| Närliggande≠ | 5 | 4 |
| Sammanfattning≠ | 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. |
| ScholarGateDatamängd ↗ |
|
|