Random subspace method

In machine learning the random subspace method,^[1] also called attribute bagging^[2] or feature bagging, is an ensemble learning method that attempts to reduce the correlation between estimators in an ensemble by training them on random samples of features instead of the entire feature set.

^ Ho, Tin Kam (1998). "The Random Subspace Method for Constructing Decision Forests" (PDF). IEEE Transactions on Pattern Analysis and Machine Intelligence. 20 (8): 832–844. doi:10.1109/34.709601. S2CID 206420153. Archived from the original (PDF) on 2019-05-14.
^ Bryll, R. (2003). "Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets". Pattern Recognition. 36 (6): 1291–1302. doi:10.1016/s0031-3203(02)00121-8.

[ho1998-1] Ho, Tin Kam (1998). "The Random Subspace Method for Constructing Decision Forests" (PDF). IEEE Transactions on Pattern Analysis and Machine Intelligence. 20 (8): 832–844. doi:10.1109/34.709601. S2CID 206420153. Archived from the original (PDF) on 2019-05-14.

[2] Bryll, R. (2003). "Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets". Pattern Recognition. 36 (6): 1291–1302. doi:10.1016/s0031-3203(02)00121-8.

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