Methoden vergleichen

	Isolation Forest	Gaußsches Mischmodell	Random Forest
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning	Machine learning
Entstehungsjahr	2008	1977	2001
Urheber	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Dempster, Laird & Rubin (EM algorithm)	Breiman, L.
Typ	Unsupervised ensemble (random partitioning trees)	Probabilistic (soft) clustering — mixture model	Ensemble (bagging of decision trees)
Wegweisende Quelle	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.	Dempster, A.P., Laird, N.M. & Rubin, D.B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–22.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Aliasnamen	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Verwandt	5	4	4
Zusammenfassung	Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets.	A Gaussian Mixture Model is a probabilistic clustering method that models the data as a weighted mixture of several Gaussian distributions, fitted with the Expectation–Maximization algorithm formalized by Dempster, Laird & Rubin in 1977. It is a generalization of K-means in which each cluster can take its own shape, size, and orientation.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.