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An Efficient Classification of Fuzzy XML Documents Based on Kernel ELM

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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.

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Notes

  1. http://www.ntu.edu.sg/home/egbhuang/

  2. http://www.cs.washington.edu/research/xmldatasets/

  3. http://abcnews.go.com/

  4. http://www.ibm.com/developerworks/develop/

  5. http://wikipedia.c3sl.ufpr.br/

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Acknowledgements

The authors wish to thank the anonymous referees for their valuable comments and suggestions, which improved the technical content and the presentation of the paper. This work was supported by the National Natural Science Foundation of China (61772269, 61370075 & 61976027) and the Scientific Research Projects of Liaoning Educational Committee (LQ2017003).

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Authors and Affiliations

  1. College of Information Science and Technology, Bohai University, Jinzhou, 121013, Liaoning, China

    Zhen Zhao

  2. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, Jiangsu, China

    Zongmin Ma & Li Yan

  3. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, 210023, Jiangsu, China

    Zongmin Ma

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  1. Zhen Zhao
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  2. Zongmin Ma
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  3. Li Yan
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Correspondence to Zongmin Ma.

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Zhao, Z., Ma, Z. & Yan, L. An Efficient Classification of Fuzzy XML Documents Based on Kernel ELM. Inf Syst Front 23, 515–530 (2021). https://doi.org/10.1007/s10796-019-09973-3

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