DEEL: an Imbalanced Binary Data Classification Method Based on Diffusion Model Data Augmentation and Multi-Objective Optimization Ensemble

Class imbalance remains a major challenge in real-world classification tasks. To address this, we propose Diffusion-Enhanced Ensemble Learning (DEEL), a unified framework that synergistically integrates diffusion-based data augmentation and multi-objective ensemble optimization for binary classification tasks. Specifically, we design a Dynamic Attention Diffusion Model (DADM) to generate diverse and realistic minority class samples through a forward noise and reverse denoising process. By incorporating temporal embeddings, residual connections, and attention mechanisms, DADM enhances the fidelity and distributional alignment of the generated data. Complementing this, an ensemble learning strategy based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimizes the fusion of multiple base classifiers across F1-score, G-mean, and AUC metrics. Extensive experiments on 26 real-world imbalanced datasets demonstrate that DEEL improves average F1-score and G-mean by 21.7 % and 24.8 %, respectively, over competitive baselines. Moreover, visualization and Jensen-Shannon distance analyses quantitatively verify the high diversity and distributional coherence of DADM-generated samples, underscoring their effectiveness for imbalanced learning.