Boosting applied to classification of mass spectral data

doi:10.6339/JDS.2003.01(4).173

科研成果详情

发表状态	已发表Published
题名	Boosting applied to classification of mass spectral data
作者	Varmuza, K.1; He, Ping 2,3; Fang, Kaitai2
发表日期	2003
发表期刊	Journal of Data Science
ISSN/eISSN	1680-743X
卷号	1 期号:4 页码:391-404
摘要	Boosting is a machine learning algorithm that is not well known in chemometrics. We apply boosting tree to the classification of mass spectral data. In the experiment, recognition of 15 chemical substructures from mass spectral data have been taken into account. The performance of boosting is very encouraging. Compared with previous result, boosting significantly improves the accuracy of classifiers based on mass spectra.
关键词	Boosting data mining decision tree mass spectra
DOI	10.6339/JDS.2003.01(4).173
URL	查看来源
语种	英语English
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://repository.uic.edu.cn/handle/39GCC9TT/5165
专题	个人在本单位外知识产出
作者单位	1.Laboratory for Chemometrics, Institute of Chemical Engineering, Vienna University of Technology, Vienna, Austria 2.Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong, P. R. China 3.Sichuan University
推荐引用方式 GB/T 7714	Varmuza, K.,He, Ping,Fang, Kaitai. Boosting applied to classification of mass spectral data[J]. Journal of Data Science, 2003, 1(4): 391-404.
APA	Varmuza, K., He, Ping, & Fang, Kaitai. (2003). Boosting applied to classification of mass spectral data. Journal of Data Science, 1(4), 391-404.
MLA	Varmuza, K.,et al."Boosting applied to classification of mass spectral data". Journal of Data Science 1.4(2003): 391-404.