The proposed GB-RVFL and GE-GB-RVFL models can be extended to handle imbalanced datasets through several strategies. One effective approach is to integrate cost-sensitive learning, where different misclassification costs are assigned to classes based on their frequency. This can be achieved by modifying the loss function in the optimization problem to penalize misclassifications of minority class samples more heavily than those of majority class samples.
Additionally, techniques such as Synthetic Minority Over-sampling Technique (SMOTE) can be employed to generate synthetic samples for the minority class, thereby balancing the dataset before training the models. This preprocessing step can enhance the model's ability to learn from underrepresented classes.
Incorporating active learning strategies can further improve the performance of the proposed models. Active learning involves iteratively selecting the most informative samples for labeling, which can be particularly beneficial in scenarios where labeling is expensive or time-consuming. The models can be adapted to identify uncertain predictions or samples that are close to the decision boundary, allowing the model to request labels for these specific instances. This targeted approach can lead to more efficient training and improved classification performance, especially in imbalanced settings.