中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information

文献类型:期刊论文

作者Lin, Xiaohui2; Yang, Fufang2; Zhou, Lina1; Yin, Peiyuan1; Kong, Hongwei1; Xing, Wenbin3; Lu, Xin1; Jia, Lewen4; Wang, Quancai2; Xu, Guowang1
刊名journal of chromatography b-analytical technologies in the biomedical and life sciences
出版日期2012-12-01
卷号910页码:149-155
关键词Artificial contrast variables Mutual information SVM-RFE Liver diseases Metabolomics
英文摘要filtering the discriminative metabolites from high dimension metabolome data is very important in metabolomics study. support vector machine-recursive feature elimination (svm-rfe) is an efficient feature selection technique and has shown promising applications in the analysis of the metabolome data. svm-rfe measures the weights of the features according to the support vectors, noise and non-informative variables in the high dimension data may affect the hyper-plane of the svm learning model. hence we proposed a mutual information (mi)-svm-rfe method which filters out noise and non-informative variables by means of artificial variables and mi, then conducts svm-rfe to select the most discriminative features. a serum metabolomics data set from patients with chronic hepatitis b, cirrhosis and hepatocellular carcinoma analyzed by liquid chromatography-mass spectrometry (lc-ms) was used to demonstrate the validation of our method. an accuracy of 74.33 +/- 2.98% to distinguish among three liver diseases was obtained, better than 72.00 +/- 4.15% from the original svm-rfe. thirty-four ion features were defined to distinguish among the control and 3 liver diseases, 17 of them were identified. (c) 2012 elsevier b.v. all rights reserved.
WOS标题词science & technology ; life sciences & biomedicine ; physical sciences
类目[WOS]biochemical research methods ; chemistry, analytical
研究领域[WOS]biochemistry & molecular biology ; chemistry
关键词[WOS]hepatoma plasma-membranes ; mass-spectrometry ; microarray data ; gene selection ; svm-rfe ; hepatocellular-carcinoma ; regenerating liver ; fatty-acids ; l-carnitine ; classification
收录类别SCI
语种英语
WOS记录号WOS:000312174700017
公开日期2015-11-10
源URL[http://159.226.238.44/handle/321008/138056]  
专题大连化学物理研究所_中国科学院大连化学物理研究所
作者单位1.Chinese Acad Sci, Dalian Inst Chem Phys, CAS Key Lab Separat Sci Analyt Chem, Dalian 116023, Peoples R China
2.Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China
3.Sixth Peoples Hosp, Dalian 116001, Peoples R China
4.Dalian Med Univ, Affiliated Hosp 1, Dept Nephrol, Dalian 116011, Peoples R China
推荐引用方式
GB/T 7714
Lin, Xiaohui,Yang, Fufang,Zhou, Lina,et al. A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information[J]. journal of chromatography b-analytical technologies in the biomedical and life sciences,2012,910:149-155.
APA Lin, Xiaohui.,Yang, Fufang.,Zhou, Lina.,Yin, Peiyuan.,Kong, Hongwei.,...&Xu, Guowang.(2012).A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information.journal of chromatography b-analytical technologies in the biomedical and life sciences,910,149-155.
MLA Lin, Xiaohui,et al."A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information".journal of chromatography b-analytical technologies in the biomedical and life sciences 910(2012):149-155.

入库方式: OAI收割

来源:大连化学物理研究所

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