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Author

Sheema Gul

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Open access Oct 2026

Robust feature selection with minimum redundancy for binary gene expression classification in machine learning

High-dimensional gene expression data often contain noisy, irrelevant, and redundant features that can reduce classification accuracy, increase computational burden, and weaken model stability. Therefore, effective feature selection is essential for improving predictive performance and interpretability in biomedical ma...

S. Safi, Sheema Gul · 0 citations
Preprint Aug 2026

ARISE: An adaptive residual-informed stability ensemble for feature selection in small-sample biomedical omics

ARISE provides a transparent, adaptive framework that jointly addresses relevance, stability, redundancy, and multiclass discrimination and its consistent results across datasets, classifiers, metrics, and feature-set sizes support further evaluation for small-sample molecular classification.

Zardad Khan, Amjad Ali, Naz Gul et al. · 0 citations

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