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

2025-05-21

AI Summary

Existing fuzzy-rough feature selection (FRFS) methods primarily aim to reduce uncertainty, yet reduced uncertainty does not necessarily translate to improved classification performance—especially in high-dimensional data, where inter-class separability is often overlooked. To address this gap, we propose a classification-margin-aware FRFS framework that jointly optimizes intra-class compactness and inter-class separability for the first time, thereby bridging the disconnect between uncertainty quantification and actual classification efficacy. Our approach integrates fuzzy-rough set theory, boundary region analysis, and margin maximization principles to construct a differentiable and optimization-friendly margin-aware feature evaluation function. Extensive experiments on 15 benchmark datasets demonstrate that the proposed method significantly outperforms six state-of-the-art algorithms, achieving superior classification accuracy, generalizability, and scalability.

Technology Category

• Machine Learning: Dimensionality Reduction/Feature Selection
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Bridging uncertainty characterization and pattern classification in feature selection
• Reducing classifier overfitting by removing redundant and irrelevant features
• Enhancing label class separation and discriminative power in feature selection

Innovation

• Margin-aware Fuzzy Rough Feature Selection framework
• Considers compactness and separation of label classes
• Enhances feature selection for better classification performance