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LF-LDA: A Topic Model for Multi-label Classification

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Advances in Internetworking, Data & Web Technologies (EIDWT 2017)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 6))

Abstract

The textual data grows explosively with the advent of the era of big data, a significant portion of textual data is text documents labeled with multi-label such as the papers with keywords. Multi-label classification is a power technology to handle the multi-labeled textual data, but a huge room stays for improving the effect of multi-label classifying for textual data. This paper introduces labeled LDA with function terms (LF-LDA), a topic model that extracts noisy function terms from textual data to improve the performance of multi-label classification. The experimental result on RCV1-v2 textual dataset shows that LF-LDA can outperform the other two state-of-art multi-label classifiers: Tuned SVM and L-LDA on both Macro-F1 and Micro-F1 metrics. The low variance also indicates LF-LDA is a robust classifier.

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Acknowledgments

This work was supported by the Chinese National Natural Science Foundation (Grant No.: 61602202), the Natural Science Foundation of Jiangsu Province, China (Grant No.: BK20160428), the Social Key Research and Development Project of Huaian, Jiangsu, China (Grant No.: HAS2015020) and the Graduate Student Scientific Research and Innovation Project of Jiangsu Province, China (Grant No.: 2015B38314).

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Authors and Affiliations

  1. Huaiyin Institute of Technology, Huaian, China

    Yongjun Zhang, Jialin Ma & Bolun Chen

  2. College of Computer and Information, Hohai University, Nanjing, China

    Yongjun Zhang, Jialin Ma, Zijian Wang & Bolun Chen

Authors
  1. Yongjun Zhang
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  2. Jialin Ma
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  3. Zijian Wang
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  4. Bolun Chen
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Corresponding author

Correspondence to Yongjun Zhang .

Editor information

Editors and Affiliations

  1. Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. School of Computer Sciences, Hubei University of Technology, Wuhan, China

    Mingwu Zhang

  3. Department of Electronic Technology, Key, Engineering University of CAPF, Xi’an, Xizang, China

    Xu An Wang

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Zhang, Y., Ma, J., Wang, Z., Chen, B. (2018). LF-LDA: A Topic Model for Multi-label Classification. In: Barolli, L., Zhang, M., Wang, X. (eds) Advances in Internetworking, Data & Web Technologies. EIDWT 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-59463-7_62

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  • DOI: https://doi.org/10.1007/978-3-319-59463-7_62

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59462-0

  • Online ISBN: 978-3-319-59463-7

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