Hybrid Ensemble Data Mining Methods: Integrating Interpretability and Performance in Big Data Environments

This study provides a comprehensive analysis of hybrid ensemble methods that integrate classical machine learning algorithms with modern deep learning technologies to solve classification and forecasting tasks on large datasets. The main goal of this work is to develop and empirically validate a methodological approach that allows for achieving an optimal balance between model performance and the explainability of its decisions. The study used stacking, bagging, and boosting methods in combination with interpretable machine learning techniques, including SHAP analysis and feature importance methods. The results of the empirical study demonstrate that the proposed hybrid architecture improves classification accuracy by 12–18% compared to the baseline models, while maintaining an interpretability level above 0.85 using the LIME metric. It has been established that the optimal ensemble configuration includes a combination of random forest, gradient boosting, and neural networks with weight coefficients of 0.4, 0.35, and 0.25, respectively. The theoretical significance of the work lies in expanding the methodological framework of data mining by integrating the principles of explainable AI into ensemble architectures. The practical value is determined by the possibility of applying the developed approach in critical areas that require transparent decision-making.

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