Compara mètodes

	Isolation Forest semi-supervisat	Detecció d'anomalies amb autoencoders
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2013–2020	2006–2014
Autor 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	Hinton, G. E. & Salakhutdinov, R. R. (autoencoders); applied to anomaly detection through multiple authors in the 2010s
Tipus	Ensemble anomaly detection (semi-supervised extension)	Unsupervised deep learning (reconstruction-based)
Font seminal	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.
Àlies	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
Relacionats	6	3
Resum	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.