Comparar métodos

	Isolation Forest Auto-supervisionado	Local Outlier Factor (LOF)
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2008–2020s	2000
Autor original	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (iForest); SSL extensions by multiple authors	Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J.
Tipo	Ensemble anomaly detector with self-supervised pre-training	Density-based anomaly detection (unsupervised)
Fonte seminal	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 413–422.	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.
Outros nomes	SSL Isolation Forest, self-supervised iForest, semi-supervised isolation forest, contrastive isolation forest	LOF, local outlier factor, density-based outlier detection, local density deviation
Relacionados	4	4
Resumo	Self-supervised Isolation Forest augments the classic Isolation Forest anomaly detector with a self-supervised pre-training stage. A pretext task — such as predicting rotation, masked features, or contrastive pairs — is solved without labels to learn a richer feature representation, which is then used when building the isolation trees, yielding sharper anomaly scores on complex, high-dimensional tabular data.	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.