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Uncertain XML documents classification using Extreme Learning Machine

Authors :
Xiangguo Zhao
Xin Bi
Guoren Wang
Zhen Zhang
Hongbo Yang
Source :
Neurocomputing. 174:375-382
Publication Year :
2016
Publisher :
Elsevier BV, 2016.

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.

Details

ISSN :
09252312
Volume :
174
Database :
OpenAIRE
Journal :
Neurocomputing
Accession number :
edsair.doi...........1b931548db712886532f6d7e3ed02fd5