INTEGRATIVE ENSEMBLE TECHNIQUES FOR ENHANCED KNOWLEDGE DISCOVERY IN DATA MINING SYSTEMS

Abstract

The growing volume and complexity of data necessitate effective knowledge discovery techniques. Knowledge discovery in data mining (KDD) has become an essential process for extracting actionable insights from large and complex datasets. Traditional single-model approaches often suffer from limitations such as overfitting, sensitivity to noise, and reduced generalizability when applied across diverse problem domains. To address these challenges, this study proposes an integrative ensemble framework that combines multiple machine learning algorithms for enhanced knowledge discovery. The framework leverages ensemble strategies— such as bagging, boosting, and stacking—while incorporating hybrid integration mechanisms to optimize predictive accuracy, robustness, and interpretability. Experimental evaluation across benchmark datasets demonstrates that the proposed approach consistently outperforms individual models in terms of classification accuracy, precision, recall, and F-measure. Furthermore, the integrative ensemble system improves the scalability and adaptability of data mining processes, making it well-suited for real-world applications in domains such as healthcare, finance, and social network analysis. This paper highlights the significance of ensemble learning as a cornerstone in the evolution of next-generation knowledge discovery systems, offering a more reliable and comprehensive pathway for decision support and intelligent analytics.

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