Enhancing Predictive Performance through Optimized Ensemble Stacking for Imbalanced Classification Problems
Keywords: Machine Learning, Ensemble stacking, Imbalanced Data
Abstract: Ensemble methods have gained significant popularity in the field of machine learning due to their ability to improve predictive performance by combining multiple models. One ensemble technique that has shown promising results is ensemble stacking, which involves training a meta-model on predictions from multiple base models. This research focused on investigating and optimizing ensemble stacking while also incorporating tailored balancing techniques for imbalanced datasets. The study explored a variety of balancing strategies, including undersampling, oversampling, and hybrid approaches, to mitigate class imbalance. Two ensemble meta-learners were considered, evaluating their ability to capture the underlying class distributions and mitigate bias while maintaining overall model performance. The research findings will contribute to the development of optimized ensemble stacking techniques for addressing imbalanced classification challenges, enabling improved decision-making and performance in real-world applications.
Submission Category: Machine learning algorithms
Submission Number: 19
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