Linganisha mbinu

	Semi-supervised Isolation Forest	Uchambuzi wa kiotomatiki wa uhalifu (Autoencoder anomaly detection)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2013–2020	2006–2014
Mwanzilishi	Extended from Liu, F.T., Ting, K.M., and Zhou, Z-H. (iForest, 2008); semi-supervised variants developed by multiple authors ca. 2013–2020	Hinton, G. E. & Salakhutdinov, R. R. (autoencoders); applied to anomaly detection through multiple authors in the 2010s
Aina	Ensemble anomaly detection (semi-supervised extension)	Unsupervised deep learning (reconstruction-based)
Chanzo asilia	Görnitz, N., Kloft, M., Rieck, K., & Brefeld, U. (2013). Toward supervised anomaly detection. Journal of Artificial Intelligence Research, 46, 235–262.	Chalapathy, R. & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.
Majina mbadala	SSIF, semi-supervised iForest, label-guided Isolation Forest, partially supervised Isolation Forest	AE anomaly detection, reconstruction-error anomaly detection, deep autoencoder outlier detection, unsupervised autoencoder anomaly detection
Zinazohusiana	6	3
Muhtasari	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.	Autoencoder anomaly detection trains a neural network to compress and then reconstruct normal data. Because the model has only ever learned what normal looks like, anomalous inputs produce noticeably higher reconstruction errors — and those errors become the anomaly score. The method requires no labeled anomalies and scales naturally to high-dimensional data such as sensor streams, images, and log records.