Multi-label learning addresses classification tasks in which each instance may be associated with multiple, non-exclusive labels. Unlike traditional single-label approaches, multi-label methods must ...

Comparative analysis of classification strategies: We evaluated the performance of four classification algorithms-MLP, SVC, RF, and XGB-across multi-label and single-label settings. The results ...