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

--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. T o 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. ITH the rapid advancement of data acquisition technologies and storage solutions, real-world data in various applications often appear in high-dimensional form, accompanied by a multitude of features. Some of these features are essential for learning processes, whereas others may be redundant or irrelevant. The presence of unnecessary features not only reduces the generalization performance of learning models but also increases computational overhead. Feature selection, guided by multiple evaluation criteria, provides an effective mechanism to eliminate irrelevant or redundant features.