Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Ensemble One-Class SVM× | Isolation Forest× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 2001 | 2008 |
| Autor original≠ | Tax, D. M. J. & Duin, R. P. W. (ensemble OC classifiers); Scholkopf et al. (OC-SVM base) | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Tipo≠ | Ensemble anomaly detector | Unsupervised ensemble (random partitioning trees) |
| Fonte seminal≠ | Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471. DOI ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Outros nomes≠ | Ensemble OC-SVM, multiple one-class SVM, OC-SVM ensemble, one-class SVM committee | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Relacionados≠ | 4 | 5 |
| Resumo≠ | Ensemble One-Class SVM combines multiple one-class support vector machine models — each trained on a different random subset of the data or features — and aggregates their anomaly scores. By pooling several OC-SVM boundary estimates, the ensemble reduces the sensitivity to kernel choice and data sampling that afflicts a single one-class SVM, producing a more stable and accurate novelty or outlier detector. | 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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