arXiv Machine Learning

RobustModelMaker: Coupling Bootstrap Stability Selection with Leakage-Safe Nested Cross-Validation for Scientific Machine Learning

arXiv:2606. 01566v1 Announce Type: new Abstract: Small-to-medium scientific datasets place machine learning pipelines under two compounding pressures.

arXiv Machine Learning

Aug 18

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

arXiv:2608. 14866v1 Announce Type: cross Abstract: Objective: Small-sample molecular classification requires feature selectors that identify predictive, stable, and nonredundant subsets for binary and multiclass outcomes.

By Zardad Khan, Amjad Ali, Naz Gul, Sheema Gul, Saeed Aldahmani

arXiv Machine Learning

Jul 17

ROOFS: RObust biOmarker Feature Selection

arXiv:2601. 05151v3 Announce Type: replace-cross Abstract: Feature selection (FS) is essential for biomarker discovery and clinical predictive modeling.

By Anastasiia Bakhmach, Paul Dufoss\'e, Simon Charpigny, Florence Monville, Laurent Greillier, Fabrice Barl\'esi, S\'ebastien Benzekry

benchmarks

arXiv Machine Learning

Aug 31

RiskBlend: A Multi-Signal Framework for Test Input Prioritization in Machine Learning Regression Testing

arXiv:2608.27704v1 Announce Type: new Abstract: When machine learning classifiers are retrained, inputs correctly classified by the previous model version may be misclassified by the updated version,...

By Madhusudan Srinivasan, Namith Nishal Raphae

arXiv Machine Learning

Aug 31

Advancing Interaction-Sensitive Feature Selection: Novel Relief-Based Algorithms, Expanded Comparisons, and Recommendations for Biomedical Data Mining

The paper refactors and expands the scikit-rebate Python package, adding new Relief‑Based Algorithm (RBA) variants such as SWRF*, mu‑Relief, and five novel methods that use alternative neighbor selection and feature scoring strategies. Benchmarking across diverse genomic simulations shows that most RBAs, except mu‑Relief, effectively detect 2‑way interactions in noisy data, with far‑scoring variants like MultiSWRFDB* excelling at interaction detection but being less sensitive to main effects. The refactored package achieves 10‑ to 35‑fold runtime reductions, and the new RBAs maintain strong performance for both main effects and 2‑way epistatic interactions, preserving predictive signals for downstream modeling.

By Kia Kazemi-Nia, Harsh Bandhey, Philip J. Freda, Ryan J. Urbanowicz

benchmarks

arXiv AI

Jul 7

EvoXplain: When Machine Learning Models Agree on Predictions but Disagree on Why -- Measuring Mechanistic Multiplicity Across Training Runs

arXiv:2512. 22240v5 Announce Type: replace-cross Abstract: Machine learning models are primarily judged by predictive performance, especially in applied genomics, where explanations are read as biological findings.

By Chama Bensmail

safety

arXiv Machine Learning

Jul 21

A multiverse-consensus pipeline for reproducible feature selection in untargeted LC-MS metabolomics

arXiv:2607. 17345v1 Announce Type: new Abstract: Background: Untargeted LC-MS metabolomics requires a long chain of preprocessing decisions, each with several equally defensible options.

By Mohammed Saeed Al-Huraibi, Ihsan Yozgat, Ahmet Kaplan

arXiv Machine Learning

Jun 11

PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework

arXiv:2505. 08784v2 Announce Type: replace-cross Abstract: As machine learning (ML) enters high-stakes domains, trustworthy uncertainty quantification (UQ) is essential for safety.

By Abhineet Agarwal, Fange Xiao, Rebecca Barter, Omer Ronen, Boyu Fan, Bin Yu

computer-vision benchmarks safety

arXiv Machine Learning

Jul 30

Feature Bagging Provides Stability

arXiv:2607. 26964v1 Announce Type: cross Abstract: We study feature bagging through the lens of algorithmic stability.

By Yuheng Ma, Qiang Sun

arXiv AI

Jul 22

FALCON-Discover: Discovering Concentrated False-Confidence Regions for Calibration

arXiv:2607. 18278v1 Announce Type: cross Abstract: Calibration is usually evaluated in aggregate, but the most dangerous failures are often local: predictions that remain highly confident despite being wrong.

By Filippo Cenacchi, Longbing Cao, Runze Yang

arXiv AI

2d ago

Model validation in machine learning: A scenario-based guide from hold-out splits to nested group cross-validation in biomedical and applied research

arXiv:2610.01284v1 Announce Type: cross Abstract: Model validation estimates the performance of a complete learning procedure on new data. However, an invalid split can produce an optimistic and stab...

By Mehmet Baygin, Sengul Dogan, Turker Tuncer

safety

arXiv Machine Learning

Aug 18

Learning Auditable Classifier Models: Source-Disjoint Tree Ensembles

arXiv:2608. 15725v1 Announce Type: new Abstract: Predictive models in clinical and regulated settings must be accurate and fully auditable.

By Srikumar Krishnamoorthy

benchmarks

arXiv Machine Learning

Jul 21

Leakage-Robust Evaluation and Data-Scale Sensitivity of Attention-Enhanced Multi-Task Learning for Joint Fault Diagnosis and Remaining Useful Life Estimation

arXiv:2607. 16493v1 Announce Type: new Abstract: Multi-task deep learning models that jointly perform fault classification and remaining useful life (RUL) regression are increasingly used in predictive maintenance, yet reported performance can be strongly affected by how sliding-window sequences are split into training and test sets.

By Md Mahamudur Rahaman Shamim, Md. Nuruzzaman, Zannatul Ferdus, Md Rajib Ahmed, Abieer Nwshad Anward, Mohammad Tooneer, Johir Uddin Khan, Khalid Hossen

benchmarks

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