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Probability based voting extreme learning machine for multiclass XML documents classification

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Abstract

This paper presents a novel solution based on Extreme Learning Machine (ELM) for multiclass XML documents classification. ELM is a generalized Single-hidden Layer Feedforward Network (SLFN) with extremely fast learning capacity. An improved vector model DSVM (Distribution based Structured Vector Model) is proposed to represent XML documents with more structural information and more precise semantic information. The XML documents classifiers are conducted based on PV-ELM (Probablity based Voting ELM) with a postprocessing method ε-RCC (ε - Revoting of Confusing Classes) to refine the voting results. To evaluate the overall performance of this solution, a series of experiments are conducted on two real datasets of news feeds online. The experimental results show that DSVM represents the XML documents more effectively and PV-ELM with ε-RCC achieves a higher accuracy than original ELM algorithm for multiclass classification.

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Authors and Affiliations

  1. Key Laboratory of Medical Image Computing (Northeastern University), Ministry of Education, Shenyang, China

    Xiangguo Zhao, Xin Bi & Baiyou Qiao

  2. College of Information Science and Engineering, Northeastern University, Liaoning, Shenyang, 110819, China

    Xiangguo Zhao, Xin Bi & Baiyou Qiao

Authors
  1. Xiangguo Zhao
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  2. Xin Bi
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  3. Baiyou Qiao
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Correspondence to Xiangguo Zhao.

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Zhao, X., Bi, X. & Qiao, B. Probability based voting extreme learning machine for multiclass XML documents classification. World Wide Web 17, 1217–1231 (2014). https://doi.org/10.1007/s11280-013-0230-8

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  • DOI: https://doi.org/10.1007/s11280-013-0230-8

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