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Multi-label Classification via Label-Topic Pairs

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Book cover Web and Big Data (APWeb-WAIM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10987))

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Abstract

The task of learning from multi-label example is rather challenging because of the tremendous number of possible label sets. It has been well recognized that exploiting label relationships in a proper way can facilitate the learning process and boost the learning performance. In this paper, we propose a novel framework called Label-Topic Pairs Multi-Label (LTPML) for multi-label classification. LTPML regards the label set associated with each instance as a document and each class label in the label set as a word and then obtains the topics from the label space by topic models. With the information about label correlations contained by topics, multi-label classification problem is decomposed into a series of single-label classification problems. Based on label-topic pairs which are constructed from relationships among the current label and topics, several multi-class classifiers are built for each class label. Two algorithms named LTPML-\(\alpha \) and LTPML-\(\beta \) are derived according to different way of selecting the topics. Experiments on benchmark data sets clearly validate the effectiveness of the proposed approaches.

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Acknowledgements

This paper is supported by the National Key Research and Development Program of China (Grant No. 2016YFB1001102), the National Natural Science Foundation of China (Grant Nos. 61502227, 61375069), the Collaborative Innovation Center of Novel Software Technology and Industrialization at Nanjing University and the Fundamental Research Funds for the Central Universities (Grant Nos. 020214380036, 020214380038).

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Authors and Affiliations

  1. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China

    Gang Chen, Yue Peng & Chongjun Wang

  2. Department of Computer Science and Technology, Nanjing University, Nanjing, 210023, China

    Gang Chen, Yue Peng & Chongjun Wang

Authors
  1. Gang Chen
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  2. Yue Peng
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  3. Chongjun Wang
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Corresponding author

Correspondence to Chongjun Wang .

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Editors and Affiliations

  1. South China University of Technology, Guangzhou, China

    Yi Cai

  2. Nagoya University, Nagoya, Japan

    Yoshiharu Ishikawa

  3. Hong Kong Baptist University, Kowloon Tong, Hong Kong, China

    Jianliang Xu

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Chen, G., Peng, Y., Wang, C. (2018). Multi-label Classification via Label-Topic Pairs. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10987. Springer, Cham. https://doi.org/10.1007/978-3-319-96890-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-96890-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96889-6

  • Online ISBN: 978-3-319-96890-2

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