Instance cloned extreme learning machine

Zhang, Y; Wu, J; Zhou, C; Cai, Z

Instance cloned extreme learning machine

Zhang, Y Wu, J Zhou, C Cai, Z

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Publication Type:: Journal Article
Citation:: Pattern Recognition, 2017, 68 pp. 52 - 65
Issue Date:: 2017-08-01

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Field	Value	Language
dc.contributor.author	Zhang, Y	en_US
dc.contributor.author	Wu, J	en_US
dc.contributor.author	Zhou, C	en_US
dc.contributor.author	Cai, Z	en_US
dc.date.issued	2017-08-01	en_US
dc.identifier.citation	Pattern Recognition, 2017, 68 pp. 52 - 65	en_US
dc.identifier.issn	0031-3203	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124969
dc.description.abstract	© 2017 Elsevier Ltd Extreme Learning Machine (ELM) is a popular machine learning method which can flexibly simulate the relationships of real-world classification applications. When facing problems (i.e., data sets) with a smaller number of samples (i.e., instances), ELM may often result in the overfitting trouble. In this paper, we propose a new Instance Cloned Extreme Learning Machine (IC-ELM for short) which can handle numerous different classification problems. IC-ELM uses an instance cloning method to balance the input data's distribution and extend the training data set, which alleviates the overfitting issue and enhances the testing classification accuracy. Experiments and comparisons on 20 UCI data sets, and validations on image and text classification applications, demonstrate that IC-ELM is able to achieve superior results compared to the original ELM algorithm and its variants, as well as several other classical machine learning algorithms.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP140100545
dc.relation	http://purl.org/au-research/grants/arc/DP140102206
dc.relation.ispartof	Pattern Recognition	en_US
dc.relation.isbasedon	10.1016/j.patcog.2017.02.036	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Instance cloned extreme learning machine	en_US
dc.type	Journal Article
utslib.citation.volume	68	en_US
utslib.for	0899 Other Information and Computing Sciences	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	68	en_US

Abstract:

© 2017 Elsevier Ltd Extreme Learning Machine (ELM) is a popular machine learning method which can flexibly simulate the relationships of real-world classification applications. When facing problems (i.e., data sets) with a smaller number of samples (i.e., instances), ELM may often result in the overfitting trouble. In this paper, we propose a new Instance Cloned Extreme Learning Machine (IC-ELM for short) which can handle numerous different classification problems. IC-ELM uses an instance cloning method to balance the input data's distribution and extend the training data set, which alleviates the overfitting issue and enhances the testing classification accuracy. Experiments and comparisons on 20 UCI data sets, and validations on image and text classification applications, demonstrate that IC-ELM is able to achieve superior results compared to the original ELM algorithm and its variants, as well as several other classical machine learning algorithms.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/124969