Uncertain XML documents classification using Extreme Learning Machine

Xiangguo Zhao*, Xin Bi, Guoren Wang, Zhen Zhang, Hongbo Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

Driven by the emerging network data exchange and storage, XML documents classification has become increasingly important. Most existing representation model and conventional learning algorithm are defined on certain XML documents. However, in many real-world applications, XML datasets contain inherent uncertainty, which brings greater challenges to classification problem. In this paper, we propose a novel solution to classify uncertain XML documents, including uncertain XML documents representation and two uncertain learning algorithms based on Extreme Learning Machine. Experimental results show that our approaches exhibit prominent performance for uncertain XML documents classification problem.

Original languageEnglish
Pages (from-to)375-382
Number of pages8
JournalNeurocomputing
Volume174
DOIs
Publication statusPublished - 22 Jan 2016
Externally publishedYes

Keywords

  • Classification
  • Extreme Learning Machine
  • Uncertain Data
  • XML

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