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| Откриване на извън-разпределени данни× | Isolation Forest× | |
|---|---|---|
| Област | Машинно обучение | Машинно обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2017 | 2008 |
| Създател≠ | Hendrycks & Gimpel | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Тип≠ | Reliability and safety method for neural networks | Unsupervised ensemble (random partitioning trees) |
| Основополагащ източник≠ | Hendrycks, D., & Gimpel, K. (2017). A baseline for detecting misclassified and out-of-distribution examples in neural networks. International Conference on Learning Representations. link ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Други названия≠ | OOD Detection, Novelty Detection, Open-Set Recognition, Dağılım Dışı Tespit | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Свързани≠ | 3 | 5 |
| Резюме≠ | Out-of-Distribution (OOD) detection is a set of techniques that identify when a deployed machine learning model receives inputs that differ significantly from its training data distribution. Introduced as a formal problem by Hendrycks and Gimpel in 2017, these methods enable models to flag unfamiliar inputs rather than silently produce unreliable predictions, making them foundational to trustworthy and safe AI deployment in high-stakes domains. | Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets. |
| ScholarGateНабор от данни ↗ |
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