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A New Text Semi-supervised Multi-label Learning Model Based on Using the Label-Feature Relations

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Computational Collective Intelligence (ICCCI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11055))

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Abstract

Multi-label learning has become popular and omnipresent in many real-world problems, especially in text classification applications, in which an instance could belong to different classes simultaneously. Due to these label constraints, there are some challenges occurring in building multi-label data. Semi-supervised learning is one possible approach to exploit abundantly unlabeled data for enhancing the classification performance with a small labeled dataset. In this paper, we propose a solution to select the most influential label based on using the relations among the labels and features to a semi-supervised multi-label classification algorithm on texts. Experiments on two datasets of Vietnamese reviews and English emails of Enron show the positive effects of the proposal.

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Notes

  1. 1.

    http://waikato.github.io/meka/

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Author information

Authors and Affiliations

  1. Vietnam National University, Hanoi (VNU), VNU-University of Engineering and Technology (UET), No. 144, Xuan Thuy, Cau Giay, Hanoi, Vietnam

    Quang-Thuy Ha, Thi-Ngan Pham, Van-Quang Nguyen, Minh-Chau Nguyen, Thanh-Huyen Pham & Tri-Thanh Nguyen

  2. The Vietnamese People’s Police Academy, Hanoi, Vietnam

    Thi-Ngan Pham

  3. Tohoku University, Sendai, Japan

    Van-Quang Nguyen

  4. Ha Long University, Ha Long, Quang Ninh, Vietnam

    Thanh-Huyen Pham

Authors
  1. Quang-Thuy Ha
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  2. Thi-Ngan Pham
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  3. Van-Quang Nguyen
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  4. Minh-Chau Nguyen
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  5. Thanh-Huyen Pham
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  6. Tri-Thanh Nguyen
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Corresponding author

Correspondence to Thi-Ngan Pham .

Editor information

Editors and Affiliations

  1. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Ngoc Thanh Nguyen

  2. Department of Computer Science and Creative Technologies, University of the West of England, Bristol, United Kingdom

    Elias Pimenidis

  3. Department of Computer Science and Creative Technologies, University of the West of England, Bristol, United Kingdom

    Zaheer Khan

  4. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Ha, QT., Pham, TN., Nguyen, VQ., Nguyen, MC., Pham, TH., Nguyen, TT. (2018). A New Text Semi-supervised Multi-label Learning Model Based on Using the Label-Feature Relations. In: Nguyen, N., Pimenidis, E., Khan, Z., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2018. Lecture Notes in Computer Science(), vol 11055. Springer, Cham. https://doi.org/10.1007/978-3-319-98443-8_37

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  • DOI: https://doi.org/10.1007/978-3-319-98443-8_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98442-1

  • Online ISBN: 978-3-319-98443-8

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