RENT -- Repeated Elastic Net Technique for Feature Selection

In this study we present the RENT feature selection method for binary classification and regression problems. We compare the performance of RENT to a number of other state-of-the-art feature selection methods on eight datasets (six for binary classification and two for regression) to illustrate RENT's performance with regard to prediction and reduction of total number of features. At its core RENT trains an ensemble of unique models using regularized elastic net to select features. Each model in the ensemble is trained with a unique and randomly selected subset from the full training data. From these models one can acquire weight distributions for each feature that contain rich information on the stability of feature selection and from which several adjustable classification criteria may be defined. Moreover, we acquire distributions of class predictions for each sample across many models in the ensemble. Analysis of these distributions may provide useful insight into which samples are more difficult to classify correctly than others. Overall, results from the tested datasets show that RENT not only can compete on-par with the best performing feature selection methods in this study, but also provides valuable insights into the stability of feature selection and sample classification.