Class imbalance remains a significant challenge in classification, often leading to poor generalization on underrepresented classes. While Oversampling methods mitigate this issue by replicating minority class instances to balance class distributions, they typically overlook the informativeness of individual samples. In this paper, we propose an entropy-guided data selection strategy that dynamically prioritizes samples exhibiting frequent prediction changes during training, that is, those with high predictive entropy. Such uncertain samples are expected to contribute more effectively to the learning process. Moreover, we incorporate a credal set-based weighting scheme that adjusts class-wise selection probabilities according to global imbalance severity, quantified using the Gini coefficient. This adjustment penalizes overrepresented classes while increasing the sampling probability of rare but uncertain examples. Experiments on benchmark datasets show that the proposed method improves overall classification performance across imbalanced data settings, while also showing a more balanced trade-off across head, body, and tail classes.
Choi et al. (2026) studied this question.