JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2022, Vol. 57 ›› Issue (8): 39-52.doi: 10.6040/j.issn.1671-9352.7.2021.141

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Multi-label feature selection with streaming and missing labels

ZHANG Zhi-hao1,2, LIN Yao-jin1,2*, LU Shun1,2, WU Yi-lin1,2, WANG Chen-xi1,2   

  1. 1. School of Computer Science, Minnan Normal University, Zhangzhou 363000, Fujian, China;
    2. Key Laboratory of Data Science and Intelligence Application, Zhangzhou 363000, Fujian, China
  • Online:2022-08-20 Published:2022-06-29

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Abstract: In the practical tasks of supervised learning, the high dimensionality of feature space, the dynamic and missing of labels bring severe challenges to supervised learning. To address these problems, a multi-label feature selection with streaming and missing labels algorithm is proposed. Firstly, to solve the impact of missing labels, the missing matrix is completed by learning label correlations. Secondly, sparse learning is utilized to select label-specific features for each newly arrived label. Then, a representative feature subset is selected by calculating the score of each label-specific features of label. Finally, a series of experiments on 11 benchmark data sets demonstrate that the proposed algorithm can effectively select a representative feature subset with better classification performance.

Key words: multi-label learning, feature selection, label-specific feature, missing label, streaming label

CLC Number: 

  • TP181
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