Reacting Imbalanced Data via Ensemble Learning Techniques

In machine learning, dealing with imbalanced datasets remains a significant challenge. Class imbalance arises when the distribution of instances across classes is uneven, which can occur in both binary and multiclass problems with varying imbalance ratios. Conventional strategies like resampling and reweighting have been commonly used to address this issue but often yield suboptimal performance and limited effectiveness. Many standard classifiers tend to bias their predictions toward the majority class, leading to reduced accuracy, particularly in recognizing minority class instances. Ensemble learning methods have shown promise in improving classification outcomes on imbalanced data. Nonetheless, these approaches often introduce considerable computational costs, extended training times, and scalability issues due to the large number of models involved, which can burden system resources. To address these limitations, this research proposes the use of pruning techniques within ensemble classifiers. By retaining only the most effective classifiers, the ensemble’s size is reduced without compromising— and potentially improving— its predictive accuracy. Experimental results indicate that the proposed method offers a viable alternative to large ensembles, producing smaller, faster, and more accurate models. The resulting pruned ensembles achieve competitive performance, with accuracy reaching up to 99%, while significantly lowering computational overhead, making them well-suited for large-scale applications.