RENT -- Repeated Elastic Net Technique for Feature Selection

TL;DR

RENT is a novel feature selection method that enhances stability and interpretability by using an ensemble of elastic net models, demonstrating balanced performance and robustness across multiple datasets.

Contribution

This paper introduces RENT, a new ensemble-based feature selection technique that improves stability and interpretability compared to existing methods.

Findings

RENT achieves a good balance between predictive performance and stability.

RENT provides valuable interpretational insights into difficult-to-predict objects.

RENT outperforms six established feature selectors on multiple datasets.

Abstract

Feature selection is an essential step in data science pipelines to reduce the complexity associated with large datasets. While much research on this topic focuses on optimizing predictive performance, few studies investigate stability in the context of the feature selection process. In this study, we present the Repeated Elastic Net Technique (RENT) for Feature Selection. RENT uses an ensemble of generalized linear models with elastic net regularization, each trained on distinct subsets of the training data. The feature selection is based on three criteria evaluating the weight distributions of features across all elementary models. This fact leads to the selection of features with high stability that improve the robustness of the final model. Furthermore, unlike established feature selectors, RENT provides valuable information for model interpretation concerning the identification of objects in the data that are difficult to predict during training. In our experiments, we benchmark RENT against six established feature selectors on eight multivariate datasets for binary classification and regression. In the experimental comparison, RENT shows a well-balanced trade-off between predictive performance and stability. Finally, we underline the additional interpretational value of RENT with an exploratory post-hoc analysis of a healthcare dataset.