Adaptive Ensemble Machine Learning Framework for Robust Prediction, Classification, and Intelligent Decision Support Across Heterogeneous Data Domains

Abstract: The increasing diversity and complexity of data across different application domains create significant challenges for conventional machine learning models in achieving robust prediction, classification, and intelligent decision support. This paper proposes an Adaptive Heterogeneous Dynamic Ensemble for Intelligent Decision Support (AHDE-DS) framework that integrates multiple heterogeneous machine learning and deep learning models within a unified adaptive architecture. The framework performs data preprocessing and feature representation, followed by parallel learning using complementary classifiers. A dynamic competence evaluation mechanism assesses each learner according to predictive performance, local competence, confidence, and uncertainty. Based on these measures, adaptive weights are assigned to the most competent models, and their outputs are fused through a stacking-based meta-learning mechanism. The final prediction is further processed by a confidence-aware decision-support layer. Experimental evaluation demonstrates that AHDE-DS achieves 98.73% accuracy, 98.61% precision, 98.54% recall, and 98.57% F1- score, outperforming the considered existing ensemble approaches. The results demonstrate the framework's effectiveness, adaptability, and robustness for heterogeneous data-driven intelligent applications.

Keywords: Adaptive Ensemble Learning, Heterogeneous Data, Dynamic Ensemble Selection, Machine Learning, Deep Learning, Stacking, Adaptive Weighting, Robust Prediction, Classification, Intelligent Decision Support