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

Fuzzy rough feature selection (FRFS) effectively alleviates the curse of dimensionality by eliminating redundant and irrelevant features, thereby improving model generalization. However, most existing algorithms focus on minimizing classification uncertainty, even though lower uncertainty does not necessarily imply stronger class discrimination or improved classification performance. This challenges the common assumption that uncertainty alone sufficiently captures feature relevance in pattern classification tasks. To bridge this gap, we propose a Margin-Aware Fuzzy Rough Feature Selection (MAFRFS) framework that explicitly incorporates structural characteristics of class distributions, namely, within-class compactness and between-class separability, into the feature evaluation process. By integrating margin-based structural cues with fuzzy rough uncertainty modeling, MAFRFS effectively guides the selection toward more separable and discriminative feature subsets. Extensive experiments reported on 23 publicly available datasets demonstrate that MAFRFS is highly scalable and more effective than FRFS. Algorithms developed under MAFRFS consistently outperform some state-of-the-art feature selection algorithms.