# Graph-Based Automatic Feature Selection for Multi-Class Classification via Mean Simplified Silhouette

arXiv: https://arxiv.org/abs/2309.02272 · HTML: https://mlsanity.com/paper/2309.02272/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 60 references that are in the map.
- First posted on arXiv 5 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [Feature Selection: A Data Perspective](https://mlsanity.com/paper/1601.07996/) (2016)
- [Infinite Feature Selection: A Graph-based Feature Filtering Approach](https://mlsanity.com/paper/2006.08184/) (2020)
- [Challenges of Feature Selection for Big Data Analytics](https://mlsanity.com/paper/1611.01875/) (2016)
- [Topological Feature Selection](https://mlsanity.com/paper/2302.09543/) (2023)
- [Adaptive Learning for the Resource-Constrained Classification Problem](https://mlsanity.com/paper/2207.09196/) (2022)
- [Graph-based Extreme Feature Selection for Multi-class Classification Tasks](https://mlsanity.com/paper/2303.01792/) (2023)

## Built on by (most cited first, 2 of 2)

- [CoSeP: Complementary Separability Pruning via Class-Separability Clustering](https://mlsanity.com/paper/2505.13225/) (2025)
- [Complementary Attention Head Pruning for Efficient Transformers](https://mlsanity.com/paper/2606.19150/) (2026)

## Abstract (arXiv)

This paper introduces a novel graph-based filter method for automatic feature selection (abbreviated as GB-AFS) for multi-class classification tasks. The method determines the minimum combination of features required to sustain prediction performance while maintaining complementary discriminating abilities between different classes. It does not require any user-defined parameters such as the number of features to select. The methodology employs the Jeffries-Matusita (JM) distance in conjunction with t-distributed Stochastic Neighbor Embedding (t-SNE) to generate a low-dimensional space reflecting how effectively each feature can differentiate between each pair of classes. The minimum number of features is selected using our newly developed Mean Simplified Silhouette (abbreviated as MSS) index, designed to evaluate the clustering results for the feature selection task. Experimental results on public data sets demonstrate the superior performance of the proposed GB-AFS over other filter-based techniques and automatic feature selection approaches. Moreover, the proposed algorithm maintained the accuracy achieved when utilizing all features, while using only 7% to 30% of the features. Consequently, this resulted in a reduction of the time needed for classifications, from 15% to 70%.