A Stacking Ensemble Learning Framework Using K-Fold Cross Validation for Accurate Binary Classification

In conclusion, stacking ensemble learning has emerged as a powerful and flexible approach for enhancing predictive accuracy and robustness across various machine learning applications. The survey demonstrates that combining diverse base learners with meta-learning strategies significantly improves generalization performance compared to traditional single-model approaches. The integration of reliable validation methods, particularly K-Fold cross-validation, further strengthens model evaluation and reduces risks associated with over-fitting and data leakage. Recent advancements in deep learning ensembles and hybrid stacking frameworks indicate a promising direction toward more scalable and adaptive predictive systems. However, challenges such as increased computational cost, interpretability concerns, and optimal model selection remain important areas for further research. Future work should focus on developing efficient, interpretable, and domain-specific stacking frameworks capable of addressing real-world data complexities while maintaining high predictive performance and practical deployment feasibility in diverse intelligent decision-making environments.

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