방법 비교

	Semi-supervised Isolation Forest	지역 이상치 계수 (Local Outlier Factor, LOF)
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2013–2020	2000
창시자	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.
유형	Ensemble anomaly detection (semi-supervised extension)	Density-based anomaly detection (unsupervised)
원전	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.
별칭	SSIF, semi-supervised iForest, label-guided Isolation Forest, partially supervised Isolation Forest	LOF, local outlier factor, density-based outlier detection, local density deviation
관련	6	4
요약	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.