A novel multi-label feature selection method based on conditional entropy and its acceleration mechanism

In multi-label learning, feature selection is a crucial step for enhancing model performance and reducing computational complexity. However, due to the interdependence among labels and the high dimensionality of feature sets, traditional single-label feature selection methods often underperform in multi-label scenarios. Moreover, many existing feature selection methods typically require a comprehensive evaluation of all features and samples in each iteration, resulting in high computational complexity. To address this issue, this paper proposes a feature selection algorithm based on fuzzy conditional entropy within the framework of fuzzy rough set theory. The method gradually identifies optimal features through iterative optimization and systematically filters out features and samples that do not contribute to the current feature subset. Specifically, the filtered features and samples are incorporated into redundant feature and sample sets, thereby dynamically excluding these redundant elements in subsequent iterations and avoiding unnecessary computations. Experiments conducted on 10 multi-label datasets demonstrate that the proposed algorithm outperforms eight other methods in terms of performance.