Random forest with self-paced bootstrap learning in lung cancer prognosis

Wang, Q; Zhou, Y; Ding, W; Zhang, Z; Muhammad, K; Cao, Z

Random forest with self-paced bootstrap learning in lung cancer prognosis

Wang, Q Zhou, Y Ding, W Zhang, Z Muhammad, K Cao, Z

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Publisher:: ASSOC COMPUTING MACHINERY
Publication Type:: Journal Article
Citation:: ACM Transactions on Multimedia Computing, Communications and Applications, 2020, 16, (1s)
Issue Date:: 2020-04-01

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Field	Value	Language
dc.contributor.author	Wang, Q
dc.contributor.author	Zhou, Y
dc.contributor.author	Ding, W
dc.contributor.author	Zhang, Z
dc.contributor.author	Muhammad, K
dc.contributor.author	Cao, Z https://orcid.org/0000-0003-3656-0328
dc.date.accessioned	2021-03-25T01:37:01Z
dc.date.available	2021-03-25T01:37:01Z
dc.date.issued	2020-04-01
dc.identifier.citation	ACM Transactions on Multimedia Computing, Communications and Applications, 2020, 16, (1s)
dc.identifier.issn	1551-6857
dc.identifier.issn	1551-6865
dc.identifier.uri	http://hdl.handle.net/10453/147517
dc.description.abstract	© 2020 ACM. Training gene expression data with supervised learning approaches can provide an alarm sign for early treatment of lung cancer to decrease death rates. However, the samples of gene features involve lots of noises in a realistic environment. In this study, we present a random forest with self-paced learning bootstrap for improvement of lung cancer classification and prognosis based on gene expression data. To be specific, we propose an ensemble learning with random forest approach to improving the model classification performance by selecting multi-classifiers. Then, we investigate the sampling strategy by gradually embedding from high- to low-quality samples by self-paced learning. The experimental results based on five public lung cancer datasets show that our proposed method could select significant genes exactly, which improves classification performance compared to that of existing approaches. We believe that our proposed method has the potential to assist doctors in gene selections and lung cancer prognosis.
dc.language	English
dc.publisher	ASSOC COMPUTING MACHINERY
dc.relation.ispartof	ACM Transactions on Multimedia Computing, Communications and Applications
dc.relation.isbasedon	10.1145/3345314
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Random forest with self-paced bootstrap learning in lung cancer prognosis
dc.type	Journal Article
utslib.citation.volume	16
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
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/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2021-03-25T01:37:00Z
pubs.issue	1s
pubs.publication-status	Published
pubs.volume	16
utslib.citation.issue	1s

Abstract:

© 2020 ACM. Training gene expression data with supervised learning approaches can provide an alarm sign for early treatment of lung cancer to decrease death rates. However, the samples of gene features involve lots of noises in a realistic environment. In this study, we present a random forest with self-paced learning bootstrap for improvement of lung cancer classification and prognosis based on gene expression data. To be specific, we propose an ensemble learning with random forest approach to improving the model classification performance by selecting multi-classifiers. Then, we investigate the sampling strategy by gradually embedding from high- to low-quality samples by self-paced learning. The experimental results based on five public lung cancer datasets show that our proposed method could select significant genes exactly, which improves classification performance compared to that of existing approaches. We believe that our proposed method has the potential to assist doctors in gene selections and lung cancer prognosis.

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http://hdl.handle.net/10453/147517