RENT -- Repeated Elastic Net Technique for Feature Selection

Feature selection is an essential step in data science pipelines to reduce the complexity of models trained on large datasets. While a major part of feature selection research focuses on optimizing predictive performance, there are only few studies that investigate the integration of feature selection stability into the feature selection process. Taking advantage of feature selection stability has the potential to enhance interpretability of machine learning models whilst maintaining predictive performance. In this study we present the RENT feature selector for binary classification and regression problems. The proposed methodology is based on an ensemble of elastic net regularized models, trained on unique subsets of the dataset. RENT selects features based on three criteria evaluating the weight distributions of features across all elementary models. Compared to conventional approaches, RENT simultaneously performs high-quality feature selection while gathering useful information for model interpretation. In addition, the proposed ensemble-based selection criteria guarantee robustness of the model by selecting features with high stability. In an experimental evaluation, we compare feature selection quality on eight multivariate datasets: six for binary classification and two for regression. We benchmark RENT against six established feature selectors. In terms of both, number of features selected and predictive performance, RENT delivers on-par results with the best performing competitors. The additional information on stability provided by RENT can be integrated in an exploratory post-hoc analysis for further insight as demonstrated in a use-case from the healthcare domain.