Multi-label feature selection is a critical dimension reduction technique in multi-label learning. In conventional multi-label feature selection methods based on information theory, feature relevance is evaluated by mutual information between candidate features and each label. However, previous methods ignore two issues: the influence of the already-selected features on the feature relevance and the influence of the correlations among labels on the feature relevance. To address these two issues, we design a new feature relevance term named Double Conditional Relevance (DCR) that employs two conditional mutual information terms to take the already-selected features and the correlations among labels into account. Finally, a novel multi-label feature selection method combining the new feature relevance term with feature redundancy term is proposed, the proposed method is named Double Conditional Relevance-Multi-label Feature Selection (DCR-MFS). Additionally, an extended method DCR-MFSarg is designed to avoid the shortcoming of the inconsistency of magnitude in DCR-MFS method. The experimental results together with theoretical analysis demonstrate the superiority of the proposed methods.

多标签特征选择是多标签学习中一种关键的降维技术。在基于信息论的常规多标签特征选择方法中，通过候选特征与每个标签之间的互信息来评估特征相关性。然而，先前的方法忽略了两个问题：已经选择的特征对特征相关性的影响以及标签之间的相关性对特征相关性的影响。为了解决这两个问题，我们设计了一个名为“双重条件相关性”（DCR）的新特征相关性术语，该术语使用两个条件互信息项来考虑已经选择的特征和标签之间的相关性。最后，提出了一种新的结合新特征相关项和特征冗余项的多标签特征选择方法，该方法被称为双条件相关多标签特征选择（DCR-MFS）。此外，设计了扩展方法DCR-MFSarg，以避免DCR-MFS方法中幅度不一致的缺点。实验结果和理论分析证明了所提方法的优越性。