Improving multi-label classification performance by label constraints

Benhui Chen, Xuefen Hong, Lihua Duan, Jinglu Hu

研究成果

9 被引用数 (Scopus)

抄録

Multi-label classification is an extension of traditional classification problem in which each instance is associated with a set of labels. For some multi-label classification tasks, labels are usually overlapped and correlated, and some implicit constraint rules are existed among the labels. This paper presents an improved multi-label classification method based on label ranking strategy and label constraints. Firstly, one-against-all decomposition technique is used to divide a multilabel classification task into multiple independent binary classification sub-problems. One binary SVM classifier is trained for each label. Secondly, based on training data, label constraint rules are mined by association rule learning method. Thirdly, a correction model based on label constraints is used to correct the probabilistic outputs of SVM classifiers for label ranking. Experiment results on three well-known multi-label benchmark datasets show that the proposed method outperforms some conventional multi-label classification methods.

本文言語English
ホスト出版物のタイトル2013 International Joint Conference on Neural Networks, IJCNN 2013
DOI
出版ステータスPublished - 2013 12 1
イベント2013 International Joint Conference on Neural Networks, IJCNN 2013 - Dallas, TX, United States
継続期間: 2013 8 42013 8 9

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks

Conference

Conference2013 International Joint Conference on Neural Networks, IJCNN 2013
国/地域United States
CityDallas, TX
Period13/8/413/8/9

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

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