Information Theory in Multi-Label Feature Selection: An Analytical Review
In the context of multi-label learning, feature selection (MLFS) is a key process for handling high-dimensional datasets, aiming to retain the most informative features while preserving inter-label relationships. This study presents an extensive overview of state-of-the-art MLFS approaches founded on principles from information theory. The paper first introduces the fundamental concepts of information theory, then provides a detailed review of representative MLFS methods along with their theoretical background. Performance assessments are carried out on real-world mu