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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