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All Journal Journal of Technology Informatics and Engineering
Joseph Teguh Santoso
Universitas Sains dan Teknologi Komputer, Semarang, Indonesia
Computer Science & IT

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Synergistic Integration of Neural Architecture Search, Ensemble Learning, and Meta-Learning with Explainable AI Migunani Migunani; Joseph Teguh Santoso; Budi Raharjo
Journal of Technology Informatics and Engineering Vol. 5 No. 1 (2026): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i1.207

Abstract

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.
Co-Authors Budi Raharjo Migunani Migunani