Compară metode

	Izolare semi-supervizată a arborilor	Factorul local de aberație (LOF)
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2013–2020	2000
Autorul original	Extended from Liu, F.T., Ting, K.M., and Zhou, Z-H. (iForest, 2008); semi-supervised variants developed by multiple authors ca. 2013–2020	Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J.
Tip	Ensemble anomaly detection (semi-supervised extension)	Density-based anomaly detection (unsupervised)
Sursa seminală	Görnitz, N., Kloft, M., Rieck, K., & Brefeld, U. (2013). Toward supervised anomaly detection. Journal of Artificial Intelligence Research, 46, 235–262.	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.
Denumiri alternative	SSIF, semi-supervised iForest, label-guided Isolation Forest, partially supervised Isolation Forest	LOF, local outlier factor, density-based outlier detection, local density deviation
Înrudite	6	4
Rezumat	Semi-supervised Isolation Forest extends the classic Isolation Forest anomaly detector by incorporating a small set of labeled anomaly (and possibly normal) examples alongside a large unlabeled dataset. This label guidance adjusts the model's anomaly scores so that known anomalies are separated more reliably, bridging the gap between fully unsupervised and fully supervised detection.	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.