SPRINT: Semi-supervised Prototypical Representation for Few-Shot Class-Incremental Tabular Learning

Researchers introduce SPRINT, the first Few-Shot Class-Incremental Learning (FSCIL) framework designed specifically for tabular data domains like cybersecurity and healthcare. The system achieves 77.37% accuracy in 5-shot learning scenarios, outperforming existing methods by 4.45% through novel semi-supervised techniques that leverage unlabeled data and confidence-based pseudo-labeling.