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An Efficient Classification of Fuzzy XML Documents Based on Kernel ELM

Author

Listed:
  • Zhen Zhao

    (Bohai University)

  • Zongmin Ma

    (Nanjing University of Aeronautics and Astronautics
    Collaborative Innovation Center of Novel Software Technology and Industrialization)

  • Li Yan

    (Nanjing University of Aeronautics and Astronautics)

Abstract

Data classification for distributed and heterogeneous XML data sources is always an open challenge. A considerable number of algorithms for classification of XML documents have been proposed in the literature. Yet, the existing approaches fall short in ability to classify the fuzzy XML documents. In this paper, we provide a KPCA-KELM classification framework for the fuzzy XML documents based on Kernel Extreme Learning Machine (KELM). Firstly, we propose a novel fuzzy XML document tree model to represent fuzzy XML documents. Secondly, we employ an effective vector space model to represent the semantic structure of fuzzy XML documents based on the proposed fuzzy XML document tree model. Thirdly, we classify the fuzzy XML document using KELM after feature extraction using Kernel Principal Component Analysis (KPCA). The corresponding experimental results demonstrate that our proposed KPCA-KELM approach shortens the training time while maintaining the same level of accuracy as the state-of-the-art baseline models.

Suggested Citation

  • Zhen Zhao & Zongmin Ma & Li Yan, 2021. "An Efficient Classification of Fuzzy XML Documents Based on Kernel ELM," Information Systems Frontiers, Springer, vol. 23(3), pages 515-530, June.
    • Handle: RePEc:spr:infosf:v:23:y:2021:i:3:d:10.1007_s10796-019-09973-3
    DOI: 10.1007/s10796-019-09973-3
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    References listed on IDEAS

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    1. Parul Gupta & Sumedha Chauhan & M. P. Jaiswal, 2019. "Classification of Smart City Research - a Descriptive Literature Review and Future Research Agenda," Information Systems Frontiers, Springer, vol. 21(3), pages 661-685, June.
    2. Girish Keshav Palshikar & Manoj Apte & Deepak Pandita, 2018. "Weakly Supervised and Online Learning of Word Models for Classification to Detect Disaster Reporting Tweets," Information Systems Frontiers, Springer, vol. 20(5), pages 949-959, October.
    3. Ting Li & Zongmin Ma, 2017. "Object-stack: An object-oriented approach for top-k keyword querying over fuzzy XML," Information Systems Frontiers, Springer, vol. 19(3), pages 669-697, June.
    4. Ting Li & Zongmin Ma, 0. "Object-stack: An object-oriented approach for top-k keyword querying over fuzzy XML," Information Systems Frontiers, Springer, vol. 0, pages 1-29.
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