Context-Regulated Q-Neutrosophic Soft Model Selection under Input Perturbation: Evidence from Three Classification Benchmarks

Authors

• Poom Kumam, Center of Excellence in Theoretical and Computational Science (TaCS-CoE) KMUTTFixed Point Research Laboratory, Departments of Mathematics, Faculty of Science, King Mongkut's University of Technology Thonburi (KMUTT), Bangkok 10140, ThailandFollow
• Supak Phiangsungnoen, General Education (Mathematics Program), Faculty of Liberal Arts, Rajamangala University of Technology Rattanakosin, Samphanthawong, Bangkok 10100, Thailand AND Institute of Research and Development, Rajamangala University of Technology Rattanakosin, 96 Phutthamonthon Sai 5 Road, Salaya,Phutthamonthon District, Nakhon Pathom 73170, ThailandFollow

Authors' ORCIDs

Poom Kumam: https://orcid.org/0000-0002-5463-4581

Supak Phiangsungnoen: https://orcid.org/0000-0003-2227-4304

Article Type

Research Article

Abstract

Selecting a classifier using a single average metric can conceal instability across validation splits, poor lower-tail behavior, and sensitivity to input perturbations. This study formulates a context-regulated Q-neutrosophic soft model-selection procedure with two fixed evaluation conditions. For each classifier and performance criterion, mean attainment (truth), across-split dispersion (indeterminacy), and the complement of a lower empirical quantile (falsity) form an auditable three-component descriptor. A bounded lower-context regulator aggregates their penalized utilities; monotonicity and perturbation bounds follow directly from its convex representation. Four fixed classifiers are compared using five benefit-oriented criteria, three repetitions of stratified five-fold cross-validation, and paired test-feature perturbations. Three publicly distributed benchmark datasets cover breast-cancer diagnostic features (569 records), wine chemistry (178 records), and handwritten-digit images (1,797 records). Under the stated weights and penalties, the radial-basis-function support vector machine ranks first on all three tasks, with composite scores 0.961179, 0.981183, and 0.979696, respectively. The difference from logistic regression on the diagnostic benchmark is only 0.000349 and reverses in some sensitivity checks. Comparisons with unpenalized aggregation, worst-context means, variability penalties, and ideal-distance ranking show that the proposed score supplies explicit uncertainty diagnostics, not demonstrated universal predictive superiority. The complete calculations and assessment-level outputs are provided for independent reproduction.

Keywords

Q-neutrosophic soft sets, Multicriteria model selection, Perturbation robustness, Classification, Reproducible benchmarking, Uncertainty

How to Cite

Kumam, Poom and Phiangsungnoen, Supak (2026) "Context-Regulated Q-Neutrosophic Soft Model Selection under Input Perturbation: Evidence from Three Classification Benchmarks," Neutrosophic Systems with Applications: Vol. 26: Iss. 10, Article 5. DOI: https://doi.org/10.63689/2993-7159.1375

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.