# Feature Selection using e-values

arXiv: https://arxiv.org/abs/2206.05391 · HTML: https://mlsanity.com/paper/2206.05391/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 65 references that are in the map.
- First posted on arXiv 11 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [Distribution-Free Predictive Inference For Regression](https://mlsanity.com/paper/1604.04173/) (2016)
- [Best Subset Selection via a Modern Optimization Lens](https://mlsanity.com/paper/1507.03133/) (2015)
- [Controlling the false discovery rate via knockoffs](https://mlsanity.com/paper/1404.5609/) (2014)
- [Bayesian model averaging: A systematic review and conceptual classification](https://mlsanity.com/paper/1509.08864/) (2015)
- [Exact computation of the halfspace depth](https://mlsanity.com/paper/1411.6927/) (2014)
- [Extremal Depth for Functional Data and Applications](https://mlsanity.com/paper/1511.00128/) (2015)
- [Generalized Fiducial Inference for Ultrahigh Dimensional Regression](https://mlsanity.com/paper/1304.7847/) (2013)

## Abstract (arXiv)

In the context of supervised parametric models, we introduce the concept of e-values. An e-value is a scalar quantity that represents the proximity of the sampling distribution of parameter estimates in a model trained on a subset of features to that of the model trained on all features (i.e. the full model). Under general conditions, a rank ordering of e-values separates models that contain all essential features from those that do not. The e-values are applicable to a wide range of parametric models. We use data depths and a fast resampling-based algorithm to implement a feature selection procedure using e-values, providing consistency results. For a p-dimensional feature space, this procedure requires fitting only the full model and evaluating p+1 models, as opposed to the traditional requirement of fitting and evaluating 2p models. Through experiments across several model settings and synthetic and real datasets, we establish that the e-values method as a promising general alternative to existing model-specific methods of feature selection.