Gene selection with guided regularized random forest

@article{Deng2013GeneSW,
  title={Gene selection with guided regularized random forest},
  author={Houtao Deng and George C. Runger},
  journal={Pattern Recognition},
  year={2013},
  volume={46},
  pages={3483-3489}
}
The regularized random forest (RRF) was recently proposed for feature selection by building only one ensemble. In RRF the features are evaluated on a part of the training data at each tree node. We derive an upper bound for the number of distinct Gini information gain values in a node, and show that many features can share the same information gain at a node with a small number of instances and a large number of features. Therefore, in a node with a small number of instances, RRF is likely to… CONTINUE READING
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