# Adaptive group-regularized logistic elastic net regression

arXiv: https://arxiv.org/abs/1805.00389 · HTML: https://mlsanity.com/paper/1805.00389/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2018.

## Built on by (most cited first, 4 of 4)

- [ControlBurn: Feature Selection by Sparse Forests](https://mlsanity.com/paper/2107.00219/) (2021)
- [Fast marginal likelihood estimation of penalties for group-adaptive elastic net](https://mlsanity.com/paper/2101.03875/) (2021)
- [ecpc: An R-package for generic co-data models for high-dimensional prediction](https://mlsanity.com/paper/2205.07640/) (2022)
- [Guiding adaptive shrinkage by co-data to improve regression-based prediction and feature selection](https://mlsanity.com/paper/2405.04917/) (2024)

## Abstract (arXiv)

In high-dimensional data settings, additional information on the features is often available. Examples of such external information in omics research are: (a) p-values from a previous study, (b) a summary of prior information, and (c) omics annotation. The inclusion of this information in the analysis may enhance classification performance and feature selection, but is not straightforward in the standard regression setting. As a solution to this problem, we propose a group-regularized (logistic) elastic net regression method, where each penalty parameter corresponds to a group of features based on the external information. The method, termed gren, makes use of the Bayesian formulation of logistic elastic net regression to estimate both the model and penalty parameters in an approximate empirical-variational Bayes framework. Simulations and an application to a colon cancer microRNA study show that, if the partitioning of the features is informative, classification performance and feature selection are indeed enhanced.