ML-CLAPPS: A Probabilistic Framework for Imbalanced Multilabel Learning under Class Prioritization

Real-world datasets often exhibit severe class imbalance, degrading the performance of deep learning models, particularly on rare classes. While imbalance is well studied in binary and multiclass settings, it is substantially more challenging in multilabel problems due to complex label co-occurrence