An Efficient Classification of Fuzzy XML Documents Based on Kernel ELM

Zhen Zhao, Zongmin Ma () and Li Yan
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Zhen Zhao: Bohai University
Zongmin Ma: Nanjing University of Aeronautics and Astronautics
Li Yan: Nanjing University of Aeronautics and Astronautics

Information Systems Frontiers, 2021, vol. 23, issue 3, No 1, 515-530

Abstract: 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.

Keywords: Data classification; Fuzzy XML; Feature extraction; Kernel extreme learning machine (KELM) (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10796-019-09973-3

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