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A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information
Lin, Xiaohui2; Yang, Fufang2; Zhou, Lina1; Yin, Peiyuan1; Kong, Hongwei1; Xing, Wenbin3; Lu, Xin1; Jia, Lewen4; Wang, Quancai2; Xu, Guowang1
KeywordArtificial Contrast Variables Mutual Information Svm-rfe Liver Diseases Metabolomics
Source PublicationJOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES
2012-12-01
DOI10.1016/j.jchromb.2012.05.020
Volume910Pages:149-155
Indexed BySCI
SubtypeArticle
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine ; Physical Sciences
WOS SubjectBiochemical Research Methods ; Chemistry, Analytical
WOS Research AreaBiochemistry & Molecular Biology ; Chemistry
WOS KeywordHEPATOMA PLASMA-MEMBRANES ; MASS-SPECTROMETRY ; MICROARRAY DATA ; GENE SELECTION ; SVM-RFE ; HEPATOCELLULAR-CARCINOMA ; REGENERATING LIVER ; FATTY-ACIDS ; L-CARNITINE ; CLASSIFICATION
AbstractFiltering 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.
Language英语
WOS IDWOS:000312174700017
Citation statistics
Cited Times:34[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://cas-ir.dicp.ac.cn/handle/321008/138056
Collection中国科学院大连化学物理研究所
Affiliation1.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
Recommended Citation
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.
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