Improving SVM based multi-label classification by using label relationship

Di Fu, Bo Zhou, Takayuki Furuzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2015-September
ISBN (Print)9781479919604, 9781479919604, 9781479919604, 9781479919604
DOIs
Publication statusPublished - 2015 Sep 28
EventInternational Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland
Duration: 2015 Jul 122015 Jul 17

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2015
CountryIreland
CityKillarney
Period15/7/1215/7/17

Keywords

  • Accuracy
  • Support vector machines

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Fu, D., Zhou, B., & Furuzuki, T. (2015). Improving SVM based multi-label classification by using label relationship. In Proceedings of the International Joint Conference on Neural Networks (Vol. 2015-September). [7280497] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2015.7280497