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Random Subspace Method

The random subspace method (RSM) (Ho, 1998) is a relatively recent method of combining models. Learning machines are trained on randomly chosen subspaces of the original input space (i.e. the training set is sampled in the feature space). The outputs of the models are then combined, usually by a simple majority vote.

Seminal Paper

HO, Tin Kam, 1998. The random subspace method for constructing decision forests, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 20, Issue 8 (Aug 1998), Pages 832-844. [Cited by 238] (28.82/year)
Abstract: "Much of previous attention on decision trees focuses on the splitting criteria and optimization of tree sizes. The dilemma between overfitting and achieving maximum accuracy is seldom resolved. A method to construct a decision tree based classifier is proposed that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity. The classifier consists of multiple trees constructed systematically by pseudorandomly selecting subsets of components of the feature vector, that is, trees constructed in randomly chosen subspaces. The subspace method is compared to single-tree classifiers and other forest construction methods by experiments on publicly available datasets, where the method's superiority is demonstrated. We also discuss independence between trees in a forest and relate that to the combined classification accuracy."