Margin-aware Fuzzy Rough Feature Selection: Bridging Uncertainty Characterization and Pattern Classification
This addresses the issue of feature selection effectiveness in high-dimensional data for pattern classification, but it is incremental as it builds on existing FRFS methods.
The paper tackles the problem that fuzzy rough feature selection (FRFS) algorithms reduce uncertainty but may not improve classification performance, by proposing a Margin-aware Fuzzy Rough Feature Selection (MAFRFS) framework that considers class compactness and separation, resulting in outperforming six state-of-the-art algorithms on 15 datasets.
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.