# Greedy feature selection: Classifier-dependent feature selection via greedy methods

arXiv: https://arxiv.org/abs/2403.05138 · HTML: https://mlsanity.com/paper/2403.05138/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 49 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [A greedy non-intrusive reduced order model for shallow water equations](https://mlsanity.com/paper/2002.11329/) (2020)
- [A critical review of LASSO and its derivatives for variable selection under dependence among covariates](https://mlsanity.com/paper/2012.11470/) (2020)
- [Physics-driven machine learning for the prediction of coronal mass ejections' travel times](https://mlsanity.com/paper/2305.10057/) (2023)

## Abstract (arXiv)

The purpose of this study is to introduce a new approach to feature ranking for classification tasks, called in what follows greedy feature selection. In statistical learning, feature selection is usually realized by means of methods that are independent of the classifier applied to perform the prediction using that reduced number of features. Instead, greedy feature selection identifies the most important feature at each step and according to the selected classifier. In the paper, the benefits of such scheme are investigated theoretically in terms of model capacity indicators, such as the Vapnik-Chervonenkis (VC) dimension or the kernel alignment, and tested numerically by considering its application to the problem of predicting geo-effective manifestations of the active Sun.