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

[2608.14866] ARISE: An adaptive residual-informed stability ensemble for feature selection in small-sample biomedical omics Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Statistics > Machine Learning arXiv:2608.14866 (stat) [Submitted on 14 Aug 2026] Title: ARISE: An adaptive residual-informed stability ensemble for feature selection in small-sample biomedical omics Authors: Zardad Khan , Amjad Ali , Naz Gul , Sheema Gul , Saeed Aldahmani View a PDF of the paper titled ARISE: An adaptive residual-informed stability ensemble for feature selection in small-sample biomedical omics, by Zardad Khan and 4 other authors View PDF HTML (experimental) Abstract: Objective: Small-sample molecular classification requires feature selectors that identify predictive, stable, and nonredundant subsets for binary and multiclass outcomes. We propose ARISE (Adaptive Residual-Informed Stability Ensemble), which integrates complementary relevance signals, class-balanced stability assessment, residual-informed redundancy control, and multiclass pairwise coverage. Methods: ARISE combines seven percentile-normalized relevance components through 15 predefined profiles, adaptively weighted by nested inner cross-validation. It was evaluated on five molecular datasets, eight feature-set sizes, three fixed classifiers (k-nearest neighbours, support vector machine, and random forest), and six filter comparators. Generalization was estimated by five-fold outer cross-validation repeated 50 times using balanced accuracy, macro-F1, and Cohen's kappa. Results: Across 210,000 held-out assessments, ARISE ranked first in all 15 dataset-metric combinations. Equal-dataset means were 0.793 for balanced accuracy, 0.776 for macro-F1, and 0.725 for kappa, exceeding the strongest aggregate comparator by 0.022, 0.023, and 0.028, respectively. Performance remained strong across compact feature sets, although the optimal budget differed by dataset. Conclusion: ARISE provides a transparent, adaptive framework that jointly addresses relevance, stability, redundancy, and multiclass discrimination. Its consistent results across datasets, classifiers, metrics, and feature-set sizes support further evaluation for small-sample molecular classification. Comments: 30 pages, 6 figures Subjects: Machine Learning (stat.ML) ; Machine Learning (cs.LG) Cite as: arXiv:2608.14866 [stat.ML] (or arXiv:2608.14866v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2608.14866 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Amjad Ali [ view email ] [v1] Fri, 14 Aug 2026 20:10:22 UTC (3,019 KB) Full-text links: Access Paper: View a PDF of the paper titled ARISE: An adaptive residual-informed stability ensemble for feature selection in small-sample biomedical omics, by Zardad Khan and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: stat.ML < prev | next > new | recent | 2026-08 Change to browse by: cs cs.LG stat References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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