Random Subspace Method Algorithm

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A Random Subspace Method (RMS) Algorithm is an Ensemble Learning Algorithm that aims to increase an ensemble's diversity by restricting classifiers to work on different random subsets of the full feature space.



  • (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Random_subspace_method Retrieved:2019-8-18.
    • In machine learning the random subspace method,[1] also called attribute bagging 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 (...)

      An ensemble of models employing the random subspace method can be constructed using the following algorithm:

      • Let the number of training points be N and the number of features in the training data be D.
      • Choose L to be the number of individual models in the ensemble.
      • For each individual model l, choose n<dub>l (nl < N) to be the number of input points for l. It is common to have only one value of nl for all the individual models.
      • For each individual model l, create a training set by choosing dl features from D with replacement and train the model.
Now, to apply the ensemble model to an unseen point, combine the outputs of the L individual models by majority voting or by combining the posterior probabilities.



  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.