BishtRef
Articles Journals Publishers Authors Subjects Most Cited New Papers Year Stats Open Access
journal article Jan 31, 2020

Random Forest with Self-Paced Bootstrap Learning in Lung Cancer Prognosis

Qingyong Wang ORCID Yun Zhou ORCID Weiping Ding ORCID Zhiguo Zhang ORCID Khan Muhammad ORCID Zehong Cao ORCID
ACM Transactions on Multimedia Computing, Communications, and Applications Vol. 16 No. 1s pp. 1-12 · Association for Computing Machinery (ACM)
View at Publisher Save 10.1145/3345314
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 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.
Topics

No keywords indexed for this article. Browse by subject →

References
42
[1]
Cancer statistics, 2018

Rebecca L. Siegel, Kimberly D. Miller, Ahmedin Jemal

CA: A Cancer Journal for Clinicians 10.3322/caac.21442
[2]
Lindsey A. Torre , Rebecca L. Siegel , and Ahmedin Jemal . 2016. Lung Cancer Statistics . Springer International Publishing . Lindsey A. Torre, Rebecca L. Siegel, and Ahmedin Jemal. 2016. Lung Cancer Statistics. Springer International Publishing.
[4]
Azian Azamimi Abdullah and Syamimi Mardiah Shaharum . 2012 . Lung cancer cell classification method using artificial neural network . Information Engineering Letters 2 , 1 (2012), 49 -- 59 . Azian Azamimi Abdullah and Syamimi Mardiah Shaharum. 2012. Lung cancer cell classification method using artificial neural network. Information Engineering Letters 2, 1 (2012), 49--59.
[9]
Qingyong Wang Liang-Yong Xia Hua Chai and Yun Zhou. 2018. Semi-supervised learning with ensemble self-training for cancer classification. In Proceedings of 2018 IEEE SmartWorld Ubiquitous Intelligence and Computing Advanced and Trusted Computing Scalable Computing and Communications Cloud and Big Data Computing Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI’18). IEEE Los Alamitos CA 796--803. Qingyong Wang Liang-Yong Xia Hua Chai and Yun Zhou. 2018. Semi-supervised learning with ensemble self-training for cancer classification. In Proceedings of 2018 IEEE SmartWorld Ubiquitous Intelligence and Computing Advanced and Trusted Computing Scalable Computing and Communications Cloud and Big Data Computing Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI’18). IEEE Los Alamitos CA 796--803. 10.1109/smartworld.2018.00149
[10]
M. Pawan Kumar Benjamin Packer and Daphne Koller. 2010. Self-paced learning for latent variable models. In Advances in Neural Information Processing Systems. 1189--1197. M. Pawan Kumar Benjamin Packer and Daphne Koller. 2010. Self-paced learning for latent variable models. In Advances in Neural Information Processing Systems. 1189--1197.
[11]
Changsheng Li , Junchi Yan , Fan Wei , Weishan Dong , Qingshan Liu , and Hongyuan Zha . 2017 . Self-paced multi-task learning . In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI’17) . 2175--2181. Changsheng Li, Junchi Yan, Fan Wei, Weishan Dong, Qingshan Liu, and Hongyuan Zha. 2017. Self-paced multi-task learning. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI’17). 2175--2181.
[16]
Zehong Cao , Weiping Ding , Yu-Kai Wang , Farookh Khadeer Hussain , Adel Al-Jumaily, and Chin-Teng Lin. 2019 . Effects of repetitive SSVEPs on EEG complexity using multiscale inherent fuzzy entropy. Neurocomputing. In press . Zehong Cao, Weiping Ding, Yu-Kai Wang, Farookh Khadeer Hussain, Adel Al-Jumaily, and Chin-Teng Lin. 2019. Effects of repetitive SSVEPs on EEG complexity using multiscale inherent fuzzy entropy. Neurocomputing. In press.
[26]
Fan Ma , Deyu Meng , Qi Xie , Zina Li , and Xuanyi Dong . 2017 . Self-paced co-training . In Proceedings of the International Conference on Machine Learning. 2275--2284 . Fan Ma, Deyu Meng, Qi Xie, Zina Li, and Xuanyi Dong. 2017. Self-paced co-training. In Proceedings of the International Conference on Machine Learning. 2275--2284.
[29]
Jesper Holst Pedersen , Witold Rzyman , Giulia Veronesi , Thomas A. D’Amico , Paul Van Schil , Laureano Molins , Gilbert Massard , and Gaetano Rocco . 2017 . Recommendations from the European Society of Thoracic Surgeons (ESTS) regarding computed tomography screening for lung cancer in Europe . European Journal of Cardio-Thoracic Surgery 51 , 3 (2017), 411 -- 420 . DOI:http://dx.doi.org/10.1093/ejcts/ezw418 10.1093/ejcts Jesper Holst Pedersen, Witold Rzyman, Giulia Veronesi, Thomas A. D’Amico, Paul Van Schil, Laureano Molins, Gilbert Massard, and Gaetano Rocco. 2017. Recommendations from the European Society of Thoracic Surgeons (ESTS) regarding computed tomography screening for lung cancer in Europe. European Journal of Cardio-Thoracic Surgery 51, 3 (2017), 411--420. DOI:http://dx.doi.org/10.1093/ejcts/ezw418
[32]
Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival among Patients with Lung Adenocarcinoma

Rahul Paul, Samuel Hawkins, Yoganand Balagurunathan et al.

Tomography 10.18383/j.tom.2016.00211
[33]
Ravi K. Samala , Heang-Ping Chan , Lubomir Hadjiiski , Mark A. Helvie , Caleb Richter , and Kenny Cha . 2018. Cross-domain and multi-task transfer learning of deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis . In Medical Imaging 2018: Computer-Aided Diagnosis , Vol. 10575 . International Society for Optics and Photonics , Bellingham, WA , 105750Q. Ravi K. Samala, Heang-Ping Chan, Lubomir Hadjiiski, Mark A. Helvie, Caleb Richter, and Kenny Cha. 2018. Cross-domain and multi-task transfer learning of deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis. In Medical Imaging 2018: Computer-Aided Diagnosis, Vol. 10575. International Society for Optics and Photonics, Bellingham, WA, 105750Q.
[34]
Te Pi , Xi Li , Zhongfei Zhang , Deyu Meng , Fei Wu , Jun Xiao , and Yueting Zhuang . 2016 . Self-paced boost learning for classification . In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI’16) . 1932--1938. Te Pi, Xi Li, Zhongfei Zhang, Deyu Meng, Fei Wu, Jun Xiao, and Yueting Zhuang. 2016. Self-paced boost learning for classification. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI’16). 1932--1938.
[35]
Lu Jiang , Deyu Meng , Qian Zhao , Shiguang Shan , and Alexander G. Hauptmann . 2015. Self-paced curriculum learning . In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI’15) , Vol. 2 . 6. Lu Jiang, Deyu Meng, Qian Zhao, Shiguang Shan, and Alexander G. Hauptmann. 2015. Self-paced curriculum learning. In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI’15), Vol. 2. 6.
[38]
Random Forests

Leo Breiman

Machine Learning 10.1023/a:1010933404324
[40]
The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance

Milton Friedman

Journal of the American Statistical Association 10.1080/01621459.1937.10503522
[42]
Zehong Cao , Chun-Hsiang Chuang , Jung-Kai King , and Chin-Teng Lin . 2019. Multi-channel EEG recordings during a sustained-attention driving task. Scientific Data 6 ( 2019 ), Article 19. Zehong Cao, Chun-Hsiang Chuang, Jung-Kai King, and Chin-Teng Lin. 2019. Multi-channel EEG recordings during a sustained-attention driving task. Scientific Data 6 (2019), Article 19.
Metrics
28
Citations
42
References
Details
Published
Jan 31, 2020
Vol/Issue
16(1s)
Pages
1-12
License
View
Authors
Q
Qingyong Wang

National University of Defense Technology, China

Y
Yun Zhou

National University of Defense Technology, China

W
Weiping Ding

Nantong University, China

Z
Zhiguo Zhang

Shenzhen University, China

K
Khan Muhammad

Sejong University, Korea

Z
Zehong Cao

University of Tasmania, Australia

Funding
National Natural Science Foundation of China Award: 61703416 and 61976120
Natural Science Foundation of Jiangsu Province Award: BK20191445
Natural Science Foundation of Hunan Province, China Award: 2018JJ3614
Qing Lan Project of Jiangsu Province
Six Talent Peaks Project of Jiangsu Province Award: XYDXXJS-048
Postgraduate Research Innovation Project from Hunan Provincial Department of Education Award: CX20190040
Cite This Article
Qingyong Wang, Yun Zhou, Weiping Ding, et al. (2020). Random Forest with Self-Paced Bootstrap Learning in Lung Cancer Prognosis. ACM Transactions on Multimedia Computing, Communications, and Applications, 16(1s), 1-12. https://doi.org/10.1145/3345314
Related

You May Also Like

Dual-path Convolutional Image-Text Embeddings with Instance Loss

Zhedong Zheng, Liang Zheng · 2020

483 citations

Understanding and Creating Art with AI: Review and Outlook

Eva Cetinic, James She · 2022

385 citations

VM-UNet: Vision Mamba UNet for Medical Image Segmentation

Jiacheng Ruan, Jixiong Li · 2025

333 citations

Applying Deep Learning for Epilepsy Seizure Detection and Brain Mapping Visualization

M. Shamim Hossain, Syed Umar Amin · 2019

257 citations

Wavelength-based Attributed Deep Neural Network for Underwater Image Restoration

Prasen Sharma, Ira Bisht · 2023

154 citations