Margin-aware Fuzzy Rough Feature Selection: Bridging Uncertainty Characterization and Pattern Classification

TL;DR

This paper introduces MAFRFS, a novel fuzzy rough feature selection framework that improves classification by considering class separation and compactness, outperforming existing methods on multiple datasets.

Contribution

The paper proposes a margin-aware fuzzy rough feature selection method that links uncertainty reduction with class separability, enhancing feature selection effectiveness.

Findings

MAFRFS outperforms six state-of-the-art algorithms.

The method is scalable to large datasets.

Extensive experiments validate its effectiveness.

Abstract

Fuzzy rough feature selection (FRFS) is an effective means of addressing the curse of dimensionality in high-dimensional data. By removing redundant and irrelevant features, FRFS helps mitigate classifier overfitting, enhance generalization performance, and lessen computational overhead. However, most existing FRFS algorithms primarily focus on reducing uncertainty in pattern classification, neglecting that lower uncertainty does not necessarily result in improved classification performance, despite it commonly being regarded as a key indicator of feature selection effectiveness in the FRFS literature. To bridge uncertainty characterization and pattern classification, we propose a Margin-aware Fuzzy Rough Feature Selection (MAFRFS) framework that considers both the compactness and separation of label classes. MAFRFS effectively reduces uncertainty in pattern classification tasks, while guiding the feature selection towards more separable and discriminative label class structures. Extensive experiments on 15 public datasets demonstrate that MAFRFS is highly scalable and more effective than FRFS. The algorithms developed using MAFRFS outperform six state-of-the-art feature selection algorithms.