Random subspace method
Random subspace method
Random subspace method (also known as feature bagging or attribute bagging) is an ensemble classifier that consists of several classifiers and outputs the class based on the outputs of the individual classifiers. Random subspace method is a generalization of the [random forest] algorithm [1]. Whereas random forests are composed of decision trees, a random subspace classifier can be composed from any underlying classifiers. TODO references
Learning algorithm
The ensemble classifier is constructed using the following algorithm:
- Let the number of training cases be N, and the number of variables in the training data be D.
- We are told the number d of input variables to be used in each individual classifier, d<D.
- For each individual classifier, choose a training set by choosing d out of D features without replacement and train the classifier.
- For classifying a new object, combine the outputs of the individual classifiers by majority voting or by combining the posterior probabilities.
References
- ^ Ho, Tin (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.