期刊
出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3345314
关键词
Lung cancer; random forest; self-paced learning; bootstrap; classification
类别
资金
- National Natural Science Foundation of China [61703416, 61976120]
- Natural Science Foundation of Hunan Province, China [2018JJ3614]
- Postgraduate Research Innovation Project fromHunan Provincial Department of Education [CX20190040]
- Natural Science Foundation of Jiangsu Province [BK20191445]
- Six Talent Peaks Project of Jiangsu Province [XYDXXJS-048]
- Qing Lan Project of Jiangsu Province
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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