Efficient feature size reduction via predictive forward selection
A new approach for faster feature selection using experience is presented. Comparison to wrapper, filter and embedded approaches on 59 datasets is provided. The difference to forward selection is statistically not significant. It requires much fewer classifier evaluations. It is statistically significant better than filter and embedded approaches.
forward selection feature selection meta-learning svm classification