Ensemble Feature Selection for Rankings of Features

In the last few years, ensemble learning has been the focus of much attention mainly in classification tasks, based on the assumption that combining the output of multiple experts is better than the output of any single expert. This idea of ensemble learning can be adapted for feature selection, in which different feature selection algorithms act as different experts. In this paper we propose an ensemble for feature selection based on combining rankings of features, trying to overcome the problem of selecting an appropriate ranker method for each problem at hand. The results of the individual rankings are combined with SVM Rank, and the adequacy of the ensemble was subsequently tested using SVM as classifier. Results on five UCI datasets showed that the use of the proposed ensemble gives better or comparable performance than the feature selection methods individually.