134905 - Random forest with self-paced bootstrap learning in lung cancer prognosis.pdf (772.61 kB)
Random forest with self-paced bootstrap learning in lung cancer prognosis
journal contribution
posted on 2023-05-20, 07:04 authored by Wang, Q, Zhou, Y, Ding, W, Zhang, Z, Muhammad, K, Cao, ZTraining 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.
History
Publication title
ACM Transactions on Multimedia Computing Communications and ApplicationsVolume
16Issue
1sArticle number
34Number
34Pagination
1-12ISSN
1551-6857Department/School
School of Information and Communication TechnologyPublisher
Association for Computing MachineryPlace of publication
United StatesRights statement
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.Repository Status
- Open