# S2FS: Spatially-Aware Separability-Driven Feature Selection in Fuzzy Decision Systems

arXiv: https://arxiv.org/abs/2509.25841 · HTML: https://mlsanity.com/paper/2509.25841/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 49 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 5 of 5)

- [RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection](https://mlsanity.com/paper/2403.05897/) (2024)
- [Fused Lasso for Feature Selection using Structural Information](https://mlsanity.com/paper/1902.09947/) (2019)
- [Identifying The Most Informative Features Using A Structurally Interacting Elastic Net](https://mlsanity.com/paper/1809.02860/) (2018)
- [Cascaded two-stage feature clustering and selection via separability and consistency in fuzzy decision systems](https://mlsanity.com/paper/2407.15893/) (2024)
- [Margin-aware Fuzzy Rough Feature Selection: Bridging Uncertainty Characterization and Pattern Classification](https://mlsanity.com/paper/2505.15250/) (2025)

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

- [Informed Machine Learning with Knowledge Landmarks](https://mlsanity.com/paper/2604.00256/) (2026)

## Abstract (arXiv)

Feature selection is crucial for fuzzy decision systems (FDSs), as it identifies informative features and eliminates rule redundancy, thereby enhancing predictive performance and interpretability. Most existing methods either fail to directly align evaluation criteria with learning performance or rely solely on non-directional Euclidean distances to capture relationships among decision classes, which limits their ability to clarify decision boundaries. However, the spatial distribution of instances has a potential impact on the clarity of such boundaries. Motivated by this, we propose Spatially-aware Separability-driven Feature Selection (S2FS), a novel framework for FDSs guided by a spatially-aware separability criterion. This criterion jointly considers within-class compactness and between-class separation by integrating scalar-distances with spatial directional information, providing a more comprehensive characterization of class structures. S2FS employs a forward greedy strategy to iteratively select the most discriminative features. Extensive experiments on ten real-world datasets demonstrate that S2FS consistently outperforms eight state-of-the-art feature selection algorithms in both classification accuracy and clustering performance, while feature visualizations further confirm the interpretability of the selected features.