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Co-learning Binary Classifiers for LP-Based Multi-label Classification

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Book cover Intelligence Science and Big Data Engineering (IScIDE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11266))

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Abstract

A simple yet practical multi-label learning method, called label powerset (LP), considers each different combination of labels that appear in the training set as a different class value of a single-label classification task. However, because those classes source from multiple labels, there may be some inherent relationships among them. To tackle this challenge, we propose a novel model which aims to co-learn binary classifiers, by combining the training of binary classifiers and the characterizing the relationship among them into a unified objective function. In addition, we develop an alternating optimization algorithm to solve the proposed problem. Extensive experimental results on various kinds of datasets well demonstrate the effectiveness of the proposed model.

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Notes

  1. 1.

    http://mulan.sourceforge.net/datasets-mlc.html.

  2. 2.

    http://cse.seu.edu.cn/PersonalPage/zhangml/files/ML-kNN.rar.

  3. 3.

    https://github.com/KKimura360/fast_RAkEL_matlab.

  4. 4.

    https://github.com/cjlin1/libsvm.

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Acknowledgments

This work was supported by NSF China (No. 61473302, 61503396).

Author information

Authors and Affiliations

  1. College of Liberal Arts and Sciences, National University of Defense Technology, Changsha, 410073, China

    Jincheng Shan, Chenping Hou, Wenzhang Zhuge & Dongyun Yi

Authors
  1. Jincheng Shan
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  2. Chenping Hou
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  3. Wenzhang Zhuge
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  4. Dongyun Yi
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Corresponding author

Correspondence to Chenping Hou .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Shanghai Jiao Tong University, Shanghai, China

    Kai Yu

  3. Tsinghua University, Beijing, China

    Jiwen Lu

  4. Central China Normal University, Wuhan, China

    Xingpeng Jiang

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Shan, J., Hou, C., Zhuge, W., Yi, D. (2018). Co-learning Binary Classifiers for LP-Based Multi-label Classification. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_39

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  • DOI: https://doi.org/10.1007/978-3-030-02698-1_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02697-4

  • Online ISBN: 978-3-030-02698-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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