Enriched random forests

@article{Amaratunga2008EnrichedRF,
  title={Enriched random forests},
  author={Dhammika Amaratunga and Javier Cabrera and Yung-Seop Lee},
  journal={Bioinformatics},
  year={2008},
  volume={24 18},
  pages={2010-4}
}
Although the random forest classification procedure works well in datasets with many features, when the number of features is huge and the percentage of truly informative features is small, such as with DNA microarray data, its performance tends to decline significantly. In such instances, the procedure can be improved by reducing the contribution of trees whose nodes are populated by non-informative features. To some extent, this can be achieved by prefiltering, but we propose a novel, yet… CONTINUE READING