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Multi-label learning has been extensively studied in many areas such as information retrieval, bioinformatics, and multimedia annotation. However, multi-label datasets often have noisy, irrelevant and redundant features with high dimensionality. Accompanying with these issues, a critical challenge is known as the curse of dimensionality. As an effective data preprocessing method, feature selection has received much attention for that it can provide a way in reducing computation time, improving prediction performance and enhancing understanding of the data. Based on this, a large number of information-theoretical-based feature selection methods are developed to solve the learning problem, i.e. multi-label classification. Unfortunately, most of existing information-theoretical-based feature selection methods are either directly transformed from single-label feature selection methods or insufficient in light of using …