Stratified sampling for feature subspace selection in random forests for high dimensional data

For high dimensional data a large portion of features are often not informative of the class of the objects. Random forest algorithms tend to use a simple random sampling of features in building their decision trees and consequently select many subspaces that contain few, if any, informative feature...

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