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

Citation

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 ISSN 1551-6857 (In Press) [Refereed Article]


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Abstract

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:Artificial Intelligence and Image Processing
Research Field:Pattern Recognition and Data Mining
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence
UTAS Author:Cao, Z (Mr Zehong Cao)
ID Code:134905
Year Published:In Press
Deposited By:Information and Communication Technology
Deposited On:2019-09-11
Last Modified:2019-09-12
Downloads:0

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