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| Półnadzorowany Isolation Forest× | Random Forest× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2013–2020 | 2001 |
| Twórca≠ | Extended from Liu, F.T., Ting, K.M., and Zhou, Z-H. (iForest, 2008); semi-supervised variants developed by multiple authors ca. 2013–2020 | Breiman, L. |
| Typ≠ | Ensemble anomaly detection (semi-supervised extension) | Ensemble (bagging of decision trees) |
| Źródło pierwotne≠ | Görnitz, N., Kloft, M., Rieck, K., & Brefeld, U. (2013). Toward supervised anomaly detection. Journal of Artificial Intelligence Research, 46, 235–262. link ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Inne nazwy | SSIF, semi-supervised iForest, label-guided Isolation Forest, partially supervised Isolation Forest | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Pokrewne≠ | 6 | 4 |
| Podsumowanie≠ | Semi-supervised Isolation Forest extends the classic Isolation Forest anomaly detector by incorporating a small set of labeled anomaly (and possibly normal) examples alongside a large unlabeled dataset. This label guidance adjusts the model's anomaly scores so that known anomalies are separated more reliably, bridging the gap between fully unsupervised and fully supervised detection. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
| ScholarGateZbiór danych ↗ |
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