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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 Type:	Article
Authors/Creators:	Wang, Q and Zhou, Y and Ding, W and Zhang, Z and Muhammad, K and Cao, Z
Keywords:	lung cancer, random forest, self-paced learning, bootstrap, classification, lung cancer prognosis
Journal or Publication Title:	ACM Transactions on Multimedia Computing Communications and Applications
Publisher:	Association for Computing Machinery
ISSN:	1551-6857
DOI / ID Number:	10.1145/3345314
Copyright Information:	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.
Related URLs:	Google Scholar: Cao, Z
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