关键词: 多标签分类, 特征选择, 多标签流形学习, Laplacian分值

Abstract: Multi-label feature selection is a feature selection technique based on multi-label data, which is an important means to improve the performance of multi-label classifiers. This paper proposes a Manifold-based Constraint Laplacian Score（M-CLS） feature selection method. The proposed method defines two kinds of Laplacian scores in two different spaces, namely the data feature space and label space. In the feature space, the similarity of the logical labels is used to modify the adjacency matrix, and a constraint laplacian score is defined. In the label space, the logical labels are extended to the numeric labels through manifold learning, and a Laplacian score is defined based on the real values of labels. The product of the two scores is the final feature evaluation index. Experiments show that the proposed method outperforms several multi-label feature selection methods.

Key words: multi-label classification, feature selection, multi-label manifold learning, Laplacian score