A Neutrosophic Random Forest Framework for Uncertainty-Aware Classification of Nursery School Applications

Abstract

This paper presents a novel framework that integrates Random Forest classification with neutrosophic logic to address the challenge of uncertainty-aware decision-making in nursery school application processes. Using the publicly available Nursery dataset—which includes socio-familial attributes such as parental occupation, financial standing, and health conditions—the proposed model not only achieves high predictive accuracy (approximately 95%) but also quantifies uncertainty explicitly through neutrosophic sets defined by truth (T), indeterminacy (I), and falsity (F) membership degrees. This approach allows for a nuanced interpretation of classification confidence, distinguishing between clear-cut cases that can be automated and borderline instances requiring human expert review. By enabling a transparent, tiered decision-making strategy, the framework enhances the fairness, explainability, and operational efficiency of admission systems, offering a practical tool for administrative use in high-stakes educational settings.