Many real-world data have multiple class labels known as multi-label data, where the labels are correlated with each other, and as such, they are not independent. Since these data are usually high-dimensional, and the current multi-label feature selection methods have not been precise enough, then a new feature selection method is necessary. In this paper, for the first time, we have modeled the problem of multi-label feature selection to a bipartite graph matching process. The proposed method constructs a bipartite graph of features (as the left vertices) and labels (as the right vertices), called Feature-Label Graph (FLG), where each feature is connected to the set of labels, where the weight of the edge between each feature and label is equal to their correlation. Then, the Hungarian algorithm estimates the best matching in FLG. The selected features in each matching are sorted by weighted correlation distance and added to the ranking vector. To select the discriminative features, the proposed method considers both the redundancy of features and the relevancy of each feature to the class labels. The results indicate the superiority of the proposed method against the other methods in classification measures.

许多真实世界的数据具有称为多标签数据的多个类标签，这些标签相互关联，因此它们不是独立的。由于这些数据通常是高维的，并且当前的多标签特征选择方法不够精确，因此需要一种新的特征选择方法。在本文中，我们首次将多标签特征选择问题建模为二部图匹配过程。所提出的方法构造特征（作为左侧顶点）和标签（作为右侧顶点）的二部图，称为特征标签图（FLG），其中每个特征都连接到标签集，边缘的权重每个特征和标签之间的关系等于它们之间的相关性。然后，匈牙利算法估计FLG中的最佳匹配。通过加权相关距离对每个匹配中的选定特征进行排序，并将其添加到排名向量中。为了选择区分特征，所提出的方法考虑了特征的冗余以及每个特征与类别标签的相关性。结果表明，该方法在分类指标上优于其他方法。