BODE: A BOUNDARY AWARE DIFFERENTIAL EVOLUTION BASED HYBRID OVERSAMPLING TECHNIQUE FOR IMBALANCED DATA CLASSIFICATION

Class imbalance presents a persistent challenge in supervised learning, often degrading classifier performance on underrepresented classes. This study introduces BODE, a hybrid oversampling method that combines boundary-aware instance selection, differential evolution-based perturbation, and density-constrained filtering. By targeting critical minority instances near decision boundaries, BODE generates diverse yet structurally valid synthetic samples. Experiments on 44 benchmark datasets using k-NN, Decision Tree, and SVM classifiers demonstrate that BODE consistently outperforms eleven widely used oversampling methods. Evaluated solely using the AUC metric, BODE achieves the highest average performance across all classifiers, with 28, 33, and 26 dataset-level wins, respectively. These results confirm BODE’s robustness and generalization capability, particularly in challenging scenarios involving overlapping or sparse decision regions.