Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (5): 65-73.DOI: 10.3778/j.issn.1002-8331.1903-0078

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Multi-Label Classification Algorithm for Weak-Label Data

WANG Jingjing, YANG Youlong   

  1. School of Mathematics and Statistics, Xidian University, Xi’an 710126, China
  • Online:2020-03-01 Published:2020-03-06



  1. 西安电子科技大学 数学与统计学院,西安 710126


For the problem of multi-label classification with incomplete label information, a new multi-label algorithm MCWD is proposed. By effectively recovering the missing label information in training data, it can produce better classification results. Firstly, in the training phase, MCWD recovers the missing label information in the training data by iteratively updating the weight of each training instance and utilizing the correlation between any two labels. Secondly, the new training set is used to train the classification model after the labels are recovered. Finally, the model is used to predict the testing set. Experimental results show that the algorithm has certain advantages on fourteen multi-label datasets.

Key words: multi-label classification, missing labels, weak label learning, label correlation



关键词: 多标签分类, 缺失标签, 弱标记学习, 标签相关性