# RENT -- Repeated Elastic Net Technique for Feature Selection

arXiv: https://arxiv.org/abs/2009.12780 · HTML: https://mlsanity.com/paper/2009.12780/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [A User-Guided Bayesian Framework for Ensemble Feature Selection in Life Science Applications (UBayFS)](https://mlsanity.com/paper/2104.14787/) (2021)
- [Automated Systems For Diagnosis of Dysgraphia in Children: A Survey and Novel Framework](https://mlsanity.com/paper/2206.13043/) (2022)
- [Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study](https://mlsanity.com/paper/2303.01389/) (2023)
- [ROOFS: RObust biOmarker Feature Selection](https://mlsanity.com/paper/2601.05151/) (2026)
- [Ranking Feature-Block Importance in Artificial Multiblock Neural Networks](https://mlsanity.com/paper/2109.10279/) (2021)
- [Towards Understanding the Survival of Patients with High-Grade Gastroenteropancreatic Neuroendocrine Neoplasms: An Investigation of Ensemble Feature Selection in the Prediction of Overall Survival](https://mlsanity.com/paper/2302.10106/) (2023)

## Abstract (arXiv)

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.