Ensemble learning for synthesis of the four diagnostics of TCM

Na Chu; Lizhuang Ma; Xiaoyu Chen; Zhiying Che; Yiyang Hu

doi:10.1109/BIBMW.2011.6112483

Ensemble learning for synthesis of the four diagnostics of TCM

Na Chu^*
, Lizhuang Ma
, Xiaoyu Chen
, Zhiying Che
, Yiyang Hu

^*此作品的通讯作者

Shanghai Jiao Tong University
Shanghai University of Traditional Chinese Medicine

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

4 引用（Scopus）

摘要

This paper outlines the procedure of synthesis of the four diagnostics of traditional Chinese medicine (TCM). It is an important part of the modernization of TCM diagnosis. We apply the principle of ensemble learning, and present a systematic framework for synthesis of four diagnostic. Especially the logistic regression and LogitBoost methods are introduced. Experiment results on chronic hepatitis B dataset demonstrate that the proposed framework is suitable to the application of TCM diagnosis in clinical, and able to obtain a smaller and satisfactory critical feature subset, 15 critical features of TCM are selected from original 123 features. The critical features are in sound agreement with those used by the physicians in making their clinical decisions. At the same time, we obtain better performance in discriminating the syndromes of CHB. The classification accuracy is 95.3153%.

源语言	英语
主期刊名	2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011
页	843-847
页数	5
DOI	https://doi.org/10.1109/BIBMW.2011.6112483
出版状态	已出版 - 2011
已对外发布	是
活动	2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011 - Atlanta, GA, 美国期限: 12 11月 2011 → 15 11月 2011

出版系列

姓名	2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011

会议

会议	2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011
国家/地区	美国
市	Atlanta, GA
时期	12/11/11 → 15/11/11

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 3 良好健康与福祉

访问文件

10.1109/BIBMW.2011.6112483

其它文件与链接

链接到 Scopus 的出版物

引用此

Chu, N., Ma, L., Chen, X., Che, Z., & Hu, Y. (2011). Ensemble learning for synthesis of the four diagnostics of TCM. 在 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011 (页码 843-847). 文章 6112483 (2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011). https://doi.org/10.1109/BIBMW.2011.6112483

@inproceedings{5cc1bb5f77ec40dd94a292bb23c892f1,

title = "Ensemble learning for synthesis of the four diagnostics of TCM",

abstract = "This paper outlines the procedure of synthesis of the four diagnostics of traditional Chinese medicine (TCM). It is an important part of the modernization of TCM diagnosis. We apply the principle of ensemble learning, and present a systematic framework for synthesis of four diagnostic. Especially the logistic regression and LogitBoost methods are introduced. Experiment results on chronic hepatitis B dataset demonstrate that the proposed framework is suitable to the application of TCM diagnosis in clinical, and able to obtain a smaller and satisfactory critical feature subset, 15 critical features of TCM are selected from original 123 features. The critical features are in sound agreement with those used by the physicians in making their clinical decisions. At the same time, we obtain better performance in discriminating the syndromes of CHB. The classification accuracy is 95.3153\%.",

keywords = "ensemble learning, synthesis of four diagnostics, traditional Chinese medicine",

author = "Na Chu and Lizhuang Ma and Xiaoyu Chen and Zhiying Che and Yiyang Hu",

year = "2011",

doi = "10.1109/BIBMW.2011.6112483",

language = "英语",

isbn = "9781457716133",

series = "2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011",

pages = "843--847",

booktitle = "2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011",

note = "2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011 ; Conference date: 12-11-2011 Through 15-11-2011",

}

Chu, N, Ma, L, Chen, X, Che, Z & Hu, Y 2011, Ensemble learning for synthesis of the four diagnostics of TCM. 在 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011., 6112483, 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011, 页码 843-847, 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011, Atlanta, GA, 美国, 12/11/11. https://doi.org/10.1109/BIBMW.2011.6112483

Ensemble learning for synthesis of the four diagnostics of TCM. / Chu, Na; Ma, Lizhuang; Chen, Xiaoyu 等.
2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011. 2011. 页码 843-847 6112483 (2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Ensemble learning for synthesis of the four diagnostics of TCM

AU - Chu, Na

AU - Ma, Lizhuang

AU - Chen, Xiaoyu

AU - Che, Zhiying

AU - Hu, Yiyang

PY - 2011

Y1 - 2011

N2 - This paper outlines the procedure of synthesis of the four diagnostics of traditional Chinese medicine (TCM). It is an important part of the modernization of TCM diagnosis. We apply the principle of ensemble learning, and present a systematic framework for synthesis of four diagnostic. Especially the logistic regression and LogitBoost methods are introduced. Experiment results on chronic hepatitis B dataset demonstrate that the proposed framework is suitable to the application of TCM diagnosis in clinical, and able to obtain a smaller and satisfactory critical feature subset, 15 critical features of TCM are selected from original 123 features. The critical features are in sound agreement with those used by the physicians in making their clinical decisions. At the same time, we obtain better performance in discriminating the syndromes of CHB. The classification accuracy is 95.3153%.

AB - This paper outlines the procedure of synthesis of the four diagnostics of traditional Chinese medicine (TCM). It is an important part of the modernization of TCM diagnosis. We apply the principle of ensemble learning, and present a systematic framework for synthesis of four diagnostic. Especially the logistic regression and LogitBoost methods are introduced. Experiment results on chronic hepatitis B dataset demonstrate that the proposed framework is suitable to the application of TCM diagnosis in clinical, and able to obtain a smaller and satisfactory critical feature subset, 15 critical features of TCM are selected from original 123 features. The critical features are in sound agreement with those used by the physicians in making their clinical decisions. At the same time, we obtain better performance in discriminating the syndromes of CHB. The classification accuracy is 95.3153%.

KW - ensemble learning

KW - synthesis of four diagnostics

KW - traditional Chinese medicine

UR - https://www.scopus.com/pages/publications/84862956100

U2 - 10.1109/BIBMW.2011.6112483

DO - 10.1109/BIBMW.2011.6112483

M3 - 会议稿件

AN - SCOPUS:84862956100

SN - 9781457716133

T3 - 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011

SP - 843

EP - 847

BT - 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011

T2 - 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011

Y2 - 12 November 2011 through 15 November 2011

ER -

Ensemble learning for synthesis of the four diagnostics of TCM

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此