The proposed feedback-guided synthetic data generation framework can be effectively combined with other algorithmic approaches for imbalanced classification to enhance performance through the following strategies:
Loss Re-weighting: Integrate loss re-weighting techniques with the feedback-guided framework to adjust the importance of different classes during training. By assigning higher weights to underrepresented classes, the model can focus more on learning from these classes, thereby improving overall classification performance. This can be particularly effective when combined with synthetic data that is generated to specifically target these underrepresented classes.
Group-Based Optimization: Implement group-based optimization strategies alongside the synthetic data generation process. This involves grouping classes based on their representation in the dataset and applying tailored optimization techniques to each group. For instance, the framework can generate synthetic samples for groups that are underrepresented and then apply group-specific loss functions or optimization algorithms to ensure that the model learns effectively from these groups.
Ensemble Methods: Combine the feedback-guided framework with ensemble learning techniques. By training multiple models on different subsets of the data (including both real and synthetic samples), the ensemble can leverage the strengths of each model, leading to improved robustness and accuracy. The feedback mechanism can be used to guide the generation of synthetic data that complements the weaknesses of individual models in the ensemble.
Active Learning: Incorporate active learning strategies where the model iteratively selects the most informative samples (both real and synthetic) for training. The feedback-guided framework can be used to generate synthetic samples that are specifically designed to challenge the model, thus enhancing the learning process. This approach ensures that the model continuously adapts to the most relevant data points.
Hybrid Approaches: Develop hybrid approaches that combine the feedback-guided framework with traditional data augmentation techniques. By augmenting real data with synthetic samples generated through the feedback mechanism, the model can benefit from a richer and more diverse training set, leading to improved generalization and performance on imbalanced datasets.
By leveraging these strategies, the proposed feedback-guided synthetic data generation framework can be effectively integrated with existing algorithmic approaches, resulting in a more comprehensive solution for tackling imbalanced classification challenges.