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Uncertain XML documents classification using Extreme Learning Machine
- 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.
- Subjects :
- Information retrieval
Uncertain data
Computer science
computer.internet_protocol
Cognitive Neuroscience
Efficient XML Interchange
XML validation
02 engineering and technology
computer.file_format
Computer Science Applications
Artificial Intelligence
XML Schema Editor
020204 information systems
ComputingMethodologies_DOCUMENTANDTEXTPROCESSING
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Binary XML
XML schema
computer
XML
Extreme learning machine
computer.programming_language
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 174
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........1b931548db712886532f6d7e3ed02fd5