Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| One-Class SVM× | Local Outlier Factor (LOF)× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 1999–2001 | 2000 |
| Autor original≠ | Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C. | Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. |
| Tipo≠ | Anomaly / novelty detection (unsupervised) | Density-based anomaly detection (unsupervised) |
| 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 ↗ | Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 93–104. DOI ↗ |
| Outros nomes | OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM | LOF, local outlier factor, density-based outlier detection, local density deviation |
| Relacionados≠ | 3 | 4 |
| Resumo≠ | One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available. | Local Outlier Factor (LOF) is a density-based, unsupervised anomaly detection algorithm introduced by Breunig, Kriegel, Ng, and Sander in 2000. It assigns each data point a continuous outlier score that quantifies how isolated that point is relative to its local neighborhood, enabling detection of anomalies that global methods miss because they blend into dense clusters elsewhere in the space. |
| ScholarGateConjunto de dados ↗ |
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