Non-uniform Feature Sampling for Decision Tree Ensembles
  We study the effectiveness of non-uniform randomized feature selection in decision tree classification. We experimentally evaluate two feature selection methodologies, based on information extracted from the provided dataset: $(i)$ \emph{leverage scores-based} and $(ii)$ \emph{norm-based} feature selection. Experimental evaluation of the proposed feature selection techniques indicate that such approaches might be more effective compared to naive uniform feature selection and moreover having comparable performance to the random forest algorithm [3] 
Anastasios Kyrillidis and Anastasios Zouzias
stat.ML cs.IT cs.LG math.IT stat.AP