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random subspace method
Random subspace method (or attribute bagging) is an ensemble classifier that consists of several classifiers each operating in a subspace of the original feature space, and outputs the class based on the outputs of these individual classifiers. Random subspace method has been used for decision trees (random decision forests),[〕〔 linear classifiers, support vector machines, nearest neighbours and other types of classifiers. This method is also applicable to one-class classifiers.]
The algorithm is an attractive choice for classification problems where the number of features is much larger than the number of training objects, such as fMRI data or gene expression data.
== Algorithm ==
The ensemble classifier is constructed using the following algorithm:
# Let the number of training objects be ''N'' and the number of features in the training data be ''D''.
# Choose ''L'' to be the number of individual classifiers in the ensemble.
# For each individual classifier l, choose ''d (d < D)'' to be the number of input variables for l. It is common to have only one value of d for all the individual classifiers
# For each individual classifier l, create a training set by choosing ''d ''features from D without replacement and train the classifier.
# For classifying a new object, combine the outputs of the ''L'' individual classifiers by majority voting or by combining the posterior probabilities.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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