Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Detecção Fora da Distribuição× | Isolation Forest× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 2017 | 2008 |
| Autor original≠ | Hendrycks & Gimpel | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Tipo≠ | Reliability and safety method for neural networks | Unsupervised ensemble (random partitioning trees) |
| Fonte seminal≠ | 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 ↗ |
| Outros nomes≠ | OOD Detection, Novelty Detection, Open-Set Recognition, Dağılım Dışı Tespit | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Relacionados≠ | 3 | 5 |
| Resumo≠ | 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. |
| ScholarGateConjunto de dados ↗ |
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