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Uncertain XML documents classification using Extreme Learning Machine
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DOI：10.1016/j.neucom.2015.02.095.png)
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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. (C) 2015 Published by Elsevier B.V.
Keyword:
Extreme Learning Machine
Classification
XML
Uncertain Data
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6.5
论文数:
2.5W
被引数:
6.5W
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