# Graph-based Extreme Feature Selection for Multi-class Classification Tasks

arXiv: https://arxiv.org/abs/2303.01792 · HTML: https://mlsanity.com/paper/2303.01792/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 3 Mar 2023.

## Builds on (most cited first, 1 of 1)

- [Infinite Feature Selection: A Graph-based Feature Filtering Approach](https://mlsanity.com/paper/2006.08184/) (2020)

## Built on by (most cited first, 1 of 1)

- [Graph-Based Automatic Feature Selection for Multi-Class Classification via Mean Simplified Silhouette](https://mlsanity.com/paper/2309.02272/) (2023)

## Abstract (arXiv)

When processing high-dimensional datasets, a common pre-processing step is feature selection. Filter-based feature selection algorithms are not tailored to a specific classification method, but rather rank the relevance of each feature with respect to the target and the task. This work focuses on a graph-based, filter feature selection method that is suited for multi-class classifications tasks. We aim to drastically reduce the number of selected features, in order to create a sketch of the original data that codes valuable information for the classification task. The proposed graph-based algorithm is constructed by combing the Jeffries-Matusita distance with a non-linear dimension reduction method, diffusion maps. Feature elimination is performed based on the distribution of the features in the low-dimensional space. Then, a very small number of feature that have complementary separation strengths, are selected. Moreover, the low-dimensional embedding allows to visualize the feature space. Experimental results are provided for public datasets and compared with known filter-based feature selection techniques.