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Summarization-Based Ensemble Learning Method for Sparsely Labeled Webpage Classification

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Web Technologies and Applications (APWeb 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9461))

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Abstract

Sparsely labeled problem has been arisen in webpage classification field recently. In this paper, we propose a summarization-based ensemble learning method for handling this problem. Directory compression strategy is used to reduce the scale of webpages to be crawled, so existing knowledge can accumulate to guide subsequent classification process. Then, smart summarization strategy is applied to get concise content of webpages, which overcomes the impact of noise and obtains more related text features. Experiments on public dataset reveal that the proposed method can obtain competing classification result when the dataset is labeled sparsely.

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Author information

Authors and Affiliations

  1. College of Information System and Management, National University of Defense Technology, Changsha, China

    Le Li, Junyi Xu & Weidong Xiao

  2. China Satellite Maritime Tracking and Control Department, Jiangyin, China

    Le Li & Haiming Tong

Authors
  1. Le Li
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  2. Junyi Xu
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  3. Haiming Tong
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  4. Weidong Xiao
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Corresponding author

Correspondence to Le Li .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, Guangdong, China

    Ruichu Cai

  2. Research Institute of China Telecom Co., Guangzhou, China

    Kang Chen

  3. Wuhan University, Wuhan, China

    Liang Hong

  4. Advanced Digital Sciences Center, Singapore, Singapore

    Xiaoyan Yang

  5. East China Normal University, Shanghai, China

    Rong Zhang

  6. Peking University, Beijing, China

    Lei Zou

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© 2015 Springer International Publishing Switzerland

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Li, L., Xu, J., Tong, H., Xiao, W. (2015). Summarization-Based Ensemble Learning Method for Sparsely Labeled Webpage Classification. In: Cai, R., Chen, K., Hong, L., Yang, X., Zhang, R., Zou, L. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9461. Springer, Cham. https://doi.org/10.1007/978-3-319-28121-6_9

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  • DOI: https://doi.org/10.1007/978-3-319-28121-6_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28120-9

  • Online ISBN: 978-3-319-28121-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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