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


Wang, Q and Zhou, Y and Ding, W and Zhang, Z and Muhammad, K and Cao, Z, Random forest with self-paced bootstrap learning in lung cancer prognosis, ACM Transactions on Multimedia Computing Communications and Applications, 16, (1s) Article 34. ISSN 1551-6857 (2020) [Refereed Article]

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Copyright 2019 Association for Computing Machinery. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.

DOI: doi:10.1145/3345314


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 proposed an ensemble learning with random forest approach to improving the model classification performance by selecting multi-classifiers. Then, we investigated 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 showed that our proposed method could select significant genes exactly, which improves classification performance compared to that in existing approaches. We believe that our proposed method has the potential to assist doctors for gene selections and lung cancer prognosis.

Item Details

Item Type:Refereed Article
Keywords:lung cancer, random forest, self-paced learning, bootstrap, classification, lung cancer prognosis
Research Division:Information and Computing Sciences
Research Group:Computer vision and multimedia computation
Research Field:Pattern recognition
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence, surveillance and space
UTAS Author:Cao, Z (Dr Zehong Cao)
ID Code:134905
Year Published:2020
Web of Science® Times Cited:15
Deposited By:Information and Communication Technology
Deposited On:2019-09-11
Last Modified:2020-09-01
Downloads:17 View Download Statistics

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