Class-Dependent Hybrid Data Augmentation for Multiclass Migraine Classification under Severe Class Imbalance
Researchers have developed a novel data augmentation framework to address severe class imbalance in migraine classification tasks. This approach corrects prior methodological flaws and introduces a hybrid strategy that assigns generation methods based on per-class sample size. Experiments on a dataset of 400 patients demonstrated that the proposed framework significantly improved classification performance, achieving a peak macro-F1 score of 0.914 with the FT-Transformer model. AI
IMPACT This research introduces a novel data augmentation technique that could improve the accuracy of AI models in medical diagnosis, particularly for conditions with imbalanced datasets.