Design Ensemble Machine Learning Model for Breast Cancer Diagnosis

Author:

Hsieh Sheau-Ling,Hsieh Sung-Huai,Cheng Po-Hsun,Chen Chi-Huang,Hsu Kai-Ping,Lee I-Shun,Wang Zhenyu,Lai Feipei

Publisher

Springer Science and Business Media LLC

Subject

Health Information Management,Health Informatics,Information Systems,Medicine (miscellaneous)

Reference14 articles.

1. Thomas, G. D., Ensemble methods in machine learning. In Proc. of the First International Workshop on Multiple Classifier System (MCS 2000), 1–15, 2000.

2. Tsymbal, A., Pechenizkiy, M., and Cunningham, P., Diversity in search strategies for ensemble feature selection. Inform. Fusion 6:83–98, 2005.

3. Xing, E. P., et al., Feature selection for high-dimensional genomic microarray data. In ICML’01: Proceedings of the Eighteenth International Conference on Machine Learning, 601–608. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2001.

4. Mitchell, T., Machine learning. McGraw-Hill, New York, 1997.

5. Yao, X., and Liu, Y., A new evolutionary system for evolving artificial neural networks. IEEE Trans. Neural Netw. 8:694–713, 1997.

Cited by 55 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Developing an ensemble machine learning study: Insights from a multi-center proof-of-concept study;PLOS ONE;2024-09-10

2. Performance Analysis of the Ensemble Model in Anaemia Detection from Unmodified Smartphone-Captured Conjunctiva Images;IETE Journal of Research;2024-06-24

3. Deep Learning based brain segmentation and tumor radiogenomic classification;Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024);2024-04-01

4. Breast Cancer Classification: In-depth Exploration of Different Paradigms and Ensemble Technique;2024 11th International Conference on Computing for Sustainable Global Development (INDIACom);2024-02-28

5. Breast cancer diagnosis using machine learning techniques;Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023);2024-02-20

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3