Multi-Label Feature Selection Method Based on Maximum Label Complexity Ratio

Multi-label feature selection, which aims to select reliable and information-rich feature subsets from high-dimensional multi-label data, plays a critical role in data mining and pattern recognition. Conventional information-theoretic methods approximate the high-order correlation between candidate features and the multi-dimensional label set by aggregating low-order mutual information between features and individual labels. However, this strategy inherently assumes all labels are equally significant, thereby overlooking their intricate distributions. To address this limitation, we first define a novel label complexity ratio based on information entropy and mutual information. We then quantify and dynamically update this ratio for each label, accounting for varying label correlations and the differential influence of selected features. Finally, we propose a new feature selection method that jointly considers the correlation with the currently most complex label, the redundancy between candidate and already-selected features, and the interaction information among these three elements to identify a high-quality feature subset. Comprehensive experiments on nine benchmark multi-label datasets demonstrate that the proposed method achieves superior performance compared to eight state-of-the-art multi-label feature selection methods.