Combining binary-SVM and pairwise label constraints for multi-label classification

Weifeng Gu*, Benhui Chen, Jinglu Hu

*この研究の対応する著者

研究成果

2 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. Recent research has shown that the ranking approach is an effective way to solve this problem. In the multi-labeled sets, classes are often related to each other. Some implicit constraint rules are existed among the labels. So we present a novel multi-label ranking algorithm inspired by the pairwise constraint rules mined from the training set to enhance the existing method. In this method, one-against-all decomposition technique is used firstly to divide a multi-label problem into binary class sub-problems. A rank list is generated by combining the probabilistic outputs of each binary Support Vector Machine (SVM) classifier. Label constraint rules are learned by minimizing the ranking loss. Experimental performance evaluation on well-known multi-label benchmark datasets show that our method improves the classification accuracy efficiently, compared with some existed methods.

本文言語English
ホスト出版物のタイトル2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010
ページ4176-4181
ページ数6
DOI
出版ステータスPublished - 2010 12 1
イベント2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010 - Istanbul, Turkey
継続期間: 2010 10 102010 10 13

出版物シリーズ

名前Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN(印刷版)1062-922X

Other

Other2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010
国/地域Turkey
CityIstanbul
Period10/10/1010/10/13

ASJC Scopus subject areas

  • 電子工学および電気工学
  • 制御およびシステム工学
  • 人間とコンピュータの相互作用

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