{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.14866",
  "author_total": 4,
  "authors": [
    {
      "name": "Zardad Khan"
    },
    {
      "name": "Amjad Ali"
    },
    {
      "name": "Naz Gul"
    },
    {
      "name": "Saeed Aldahmani"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/caheyz0h9tk1c.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.14866",
  "fulltext_url": null,
  "id": "caheyz0h9tk1c",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.14866",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1312|Gene expression and cancer classification"
    ]
  ],
  "openalex": "W7203691003",
  "pdf_url": "https://arxiv.org/pdf/2608.14866",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/caheyz0h9tk1c.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:13Z",
      "native_id": "2608.14866",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.14866"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "ARISE: An adaptive residual-informed stability ensemble for feature selection in small-sample biomedical omics",
  "title_text": "ARISE: An adaptive residual-informed stability ensemble for feature selection in small-sample biomedical omics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:13Z",
  "url": "https://research.draconitic.com/paper/caheyz0h9tk1c",
  "venue": "arXiv",
  "versions": []
}