Improving SVM based multi-label classification by using label relationship

Di Fu, Bo Zhou, Takayuki Furuzuki

研究成果: Conference contribution

7 被引用数 (Scopus)


This paper proposes an improved SVM based multi-label classification method by using relationship among labels. Following a traditional multi-label solution, binary relevance (BR) method is first used to decompose the multi-label classification problem into multiple binary classification sub-problems, each of which is solved by an SVM classifier. By using Platt's sigmoid technique, each SVM classifier gives probability output for the following correction. A probability model is introduced to estimate the relationship among labels. The extracted label relationship is then applied to correct the outputs of SVM classifiers, in which a dynamic weight strategy is further introduced. Numerical experiments on widely used benchmark datasets show that the proposed method can improve the accuracy of multi-label classification when compared with traditional BR method and some other conventional multi-label classification methods.

ホスト出版物のタイトルProceedings of the International Joint Conference on Neural Networks
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(印刷版)9781479919604, 9781479919604, 9781479919604, 9781479919604
出版ステータスPublished - 2015 9 28
イベントInternational Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland
継続期間: 2015 7 122015 7 17


OtherInternational Joint Conference on Neural Networks, IJCNN 2015

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能


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