Enhancing multi-label classification based on local label constraints and classifier chains

Benhui Chen, Weite Li, Yuqing Zhang, Takayuki Furuzuki

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

1 Citation (Scopus)

Abstract

In the multi-label classification issue, some implicit constraints and dependencies are always existed among labels. Exploring the correlation information among different labels is important for many applications. It not only can enhance the classifier performance but also can help to interpret the classification results for some specific applications. This paper presents an improved multi-label classification method based on local label constraints and classifier chains for solving multi-label tasks with large number of labels. Firstly, in order to exploit local label constraints in multi-label problem with large number of labels, clustering approach is utilized to segment training label set into several subsets. Secondly, for each label subset, local tree-structure constraints among different labels are mined based on mutual information metric. Thirdly, based on the mined local tree-structure label constraints, a variant of classifier chain strategy is implemented to enhance the multi-label learning system. Experiment results on five multi-label benchmark datasets show that the proposed method is a competitive approach for solving multi-label classification tasks with large number of labels.

Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks, IJCNN 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1458-1463
Number of pages6
Volume2016-October
ISBN (Electronic)9781509006199
DOIs
Publication statusPublished - 2016 Oct 31
Event2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada
Duration: 2016 Jul 242016 Jul 29

Other

Other2016 International Joint Conference on Neural Networks, IJCNN 2016
CountryCanada
CityVancouver
Period16/7/2416/7/29

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Labels
Classifiers
Learning systems

Keywords

  • Classifier chains
  • Label constraints
  • Multi-label classification

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Chen, B., Li, W., Zhang, Y., & Furuzuki, T. (2016). Enhancing multi-label classification based on local label constraints and classifier chains. In 2016 International Joint Conference on Neural Networks, IJCNN 2016 (Vol. 2016-October, pp. 1458-1463). [7727370] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2016.7727370

Enhancing multi-label classification based on local label constraints and classifier chains. / Chen, Benhui; Li, Weite; Zhang, Yuqing; Furuzuki, Takayuki.

2016 International Joint Conference on Neural Networks, IJCNN 2016. Vol. 2016-October Institute of Electrical and Electronics Engineers Inc., 2016. p. 1458-1463 7727370.

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

Chen, B, Li, W, Zhang, Y & Furuzuki, T 2016, Enhancing multi-label classification based on local label constraints and classifier chains. in 2016 International Joint Conference on Neural Networks, IJCNN 2016. vol. 2016-October, 7727370, Institute of Electrical and Electronics Engineers Inc., pp. 1458-1463, 2016 International Joint Conference on Neural Networks, IJCNN 2016, Vancouver, Canada, 16/7/24. https://doi.org/10.1109/IJCNN.2016.7727370
Chen B, Li W, Zhang Y, Furuzuki T. Enhancing multi-label classification based on local label constraints and classifier chains. In 2016 International Joint Conference on Neural Networks, IJCNN 2016. Vol. 2016-October. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1458-1463. 7727370 https://doi.org/10.1109/IJCNN.2016.7727370
Chen, Benhui ; Li, Weite ; Zhang, Yuqing ; Furuzuki, Takayuki. / Enhancing multi-label classification based on local label constraints and classifier chains. 2016 International Joint Conference on Neural Networks, IJCNN 2016. Vol. 2016-October Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1458-1463
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