Introduces a novel minority-aware and robust feature selection framework, GenMWS, for high-dimensional class-imbalanced gene expression data.

Constructs class-specific core intervals and weighted own-class/other-class membership scores to capture gene-wise class structure.

Develops a weighted nonoverlap mask and class-specific support summaries to emphasize minority-class informative patterns while controlling majority-supported structure.