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

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dc.contributor.authorWang, Q.
dc.contributor.authorZhou, Y.
dc.contributor.authorDing, W.
dc.contributor.authorZhang, Z.
dc.contributor.authorMuhammad, K.
dc.contributor.authorCao, Z.
dc.date.issued2020
dc.descriptionLink to a related website: https://unpaywall.org/10.1145/3345314, Open Access via Unpaywall
dc.description.abstract<jats:p>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.</jats:p>
dc.identifier.citationACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP), 2020; 16(1s)
dc.identifier.doi10.1145/3345314
dc.identifier.issn1551-6857
dc.identifier.issn1551-6865
dc.identifier.orcidCao, Z. [0000-0003-3656-0328]
dc.identifier.urihttps://hdl.handle.net/11541.2/27276
dc.language.isoen
dc.publisherASSOC COMPUTING MACHINERY
dc.relation.fundingNational Natural Science Foundation of China 61703416
dc.relation.fundingNatural Science Foundation of Hunan Province, China 2018JJ3614
dc.relation.fundingPostgraduate Research Innovation Project fromHunan Provincial Department of Education CX20190040
dc.relation.fundingNatural Science Foundation of Jiangsu Province BK20191445
dc.relation.fundingSix Talent Peaks Project of Jiangsu Province XYDXXJS-048
dc.relation.fundingQing Lan Project of Jiangsu Province
dc.rightsCopyright 2020 Association for Computing Machinery
dc.source.urihttps://doi.org/10.1145/3345314
dc.titleRandom forest with self-paced bootstrap learning in lung cancer prognosis
dc.typeJournal article
pubs.publication-statusPublished
ror.mmsid9916606127101831

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