Voting base online sequential extreme learning machine for multi-class classification
In this paper, we propose a voting based online sequential extreme learning machine (VOS-ELM) for single hidden layer feedforward networks (SLFNs) to perform the online sequential multi-class classification. Utilizing the recent voting based extreme learning machine (V-ELM) and the online sequential...
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sg-ntu-dr.10356-1029452020-03-07T13:24:51Z Voting base online sequential extreme learning machine for multi-class classification Cao, Jiuwen Lin, Zhiping Huang, Guang-Bin School of Electrical and Electronic Engineering IEEE International Symposium on Circuits and Systems (2013 : Beijing, China) DRNTU::Engineering::Electrical and electronic engineering In this paper, we propose a voting based online sequential extreme learning machine (VOS-ELM) for single hidden layer feedforward networks (SLFNs) to perform the online sequential multi-class classification. Utilizing the recent voting based extreme learning machine (V-ELM) and the online sequential extreme learning machine (OS-ELM), the newly developed VOS-ELM is able to classify online sequences by learning data one-by-one or chunk-by-chunk with fixed or varying chunk size and to reach a higher classification accuracy than the original OS-ELM. Simulations on several real world classification datasets show that VOS-ELM outperforms OS-ELM as well as several state-of-the-art online sequential algorithms. 2013-10-25T02:46:28Z 2019-12-06T21:02:35Z 2013-10-25T02:46:28Z 2019-12-06T21:02:35Z 2013 2013 Conference Paper Cao, J., Lin, Z., & Huang, G. B. (2013). Voting base online sequential extreme learning machine for multi-class classification. 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013), 2327-2330. https://hdl.handle.net/10356/102945 http://hdl.handle.net/10220/16887 10.1109/ISCAS.2013.6572344 en |
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DRNTU::Engineering::Electrical and electronic engineering Cao, Jiuwen Lin, Zhiping Huang, Guang-Bin Voting base online sequential extreme learning machine for multi-class classification |
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In this paper, we propose a voting based online sequential extreme learning machine (VOS-ELM) for single hidden layer feedforward networks (SLFNs) to perform the online sequential multi-class classification. Utilizing the recent voting based extreme learning machine (V-ELM) and the online sequential extreme learning machine (OS-ELM), the newly developed VOS-ELM is able to classify online sequences by learning data one-by-one or chunk-by-chunk with fixed or varying chunk size and to reach a higher classification accuracy than the original OS-ELM. Simulations on several real world classification datasets show that VOS-ELM outperforms OS-ELM as well as several state-of-the-art online sequential algorithms. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Cao, Jiuwen Lin, Zhiping Huang, Guang-Bin |
format |
Conference or Workshop Item |
author |
Cao, Jiuwen Lin, Zhiping Huang, Guang-Bin |
author_sort |
Cao, Jiuwen |
title |
Voting base online sequential extreme learning machine for multi-class classification |
title_short |
Voting base online sequential extreme learning machine for multi-class classification |
title_full |
Voting base online sequential extreme learning machine for multi-class classification |
title_fullStr |
Voting base online sequential extreme learning machine for multi-class classification |
title_full_unstemmed |
Voting base online sequential extreme learning machine for multi-class classification |
title_sort |
voting base online sequential extreme learning machine for multi-class classification |
publishDate |
2013 |
url |
https://hdl.handle.net/10356/102945 http://hdl.handle.net/10220/16887 |
_version_ |
1681037130428579840 |