Feature selection (FS) for multi-label text classification faces issues such as high dimensionality, strong label correlations, and sparse features, which often lead to suboptimal feature subsets.

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 ...

Researchers at Lorestan University have combined ant colony optimization, reinforcement learning, and opposition-based ...

A computational study on 10 benchmark datasets found that MFLRA outperformed eight comparison methods by combining multi-order relevance, label uncertainty and feature interactions.