Synergistic Integration of Neural Architecture Search, Ensemble Learning, and Meta-Learning with Explainable AI - Migunani Migunani; Joseph Teguh Santoso; Budi Raharjo (University of Science and Computer Technology, 2026)
The automation of deep learning model design through Neural Architecture Search (NAS) has emerged as a transformative paradigm, yet the integration of NAS with ensemble learning and meta-learning remains underexplored. This study presents a comprehensive framework that synergistically combines (1) NAS for automated neural network architecture discovery, (2) homogeneous ensemble learning through NAS-generated architectures, (3) heterogeneous ensemble learning integrating diverse base learners, and (4) meta-learning with stacked generalization for adaptive model fusion, all complemented by Explainable AI (XAI) via SHAP for model interpretability. The proposed framework is evaluated on four benchmark datasets: Iris, Wine, Breast Cancer, and Digits. Experimental results demonstrate that the heterogeneous ensemble achieves competitive or superior performance across all datasets, with cross-validation accuracy reaching 98.87% (wine), 97.72% (breast cancer), 98.05% (digits), and 95.33% (iris). SHAP-based explainability analysis reveals consistent feature importance patterns across NAS architectures, providing valuable interpretability insights. The framework establishes a robust pipeline for automated, accurate, and interpretable machine learning, addressing the growing demand for transparent AI systems in critical applications.