Journal
NEUROCOMPUTING
Volume 174, Issue -, Pages 194-202Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2015.05.121
Keywords
Extreme learning machine; Classification; Uncertain data; Single hidden layer feedforward neural networks
Categories
Funding
- NSFC [61173029, 61472069, 61332006, 60933001, 75105487, 61100024]
- National Basic Research Program of China (973) [2011CB302200-G]
- National High Technology Research and Development 863 Program of China [2012AA011004]
- Fundamental Research Funds for the Central Universities [N110404011]
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In recent years, along with the generation of uncertain data, more and more attention is paid to the mining of uncertain data. In this paper, we study the problem of classifying uncertain data using Extreme Learning Machine (ELM). We first propose the UU-ELM algorithm for classification of uncertain data which is uniformly distributed. Furthermore, the NU-ELM algorithm is proposed for classifying uncertain data which are non-uniformly distributed. By calculating bounds of the probability, the efficiency of the algorithm can be improved. Finally, the performances of our methods are verified through a large number of simulated experiments. The experimental results show that our methods are effective ways to solve the problem of uncertain data classification, reduce the execution time and improve the efficiency. (C) 2015 Elsevier B.V. All rights reserved.
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