Non-uniform Feature Sampling for Decision Tree Ensembles
This work addresses feature selection for decision tree ensembles, offering incremental improvements over existing methods.
The paper tackled the problem of feature selection in decision tree classification by proposing non-uniform randomized feature selection methods based on leverage scores and norms, showing they are more effective than uniform selection and comparable to random forests.
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]