A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information

► A new method was proposed to select the discriminative variables from the high dimension metabolome data. ► The developed method filters out noise and non-informative variables by means of artificial variables and mutual information. ► The discriminative variables were selected by SVM-RFE after re...

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Veröffentlicht in:	Journal of chromatography. B, Analytical technologies in the biomedical and life sciences Analytical technologies in the biomedical and life sciences, 2012-12, Vol.910, p.149-155
Hauptverfasser:	Lin, Xiaohui, Yang, Fufang, Zhou, Lina, Yin, Peiyuan, Kong, Hongwei, Xing, Wenbin, Lu, Xin, Jia, Lewen, Wang, Quancai, Xu, Guowang
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial contrast variables blood serum Chromatography, High Pressure Liquid chronic hepatitis B data collection Data Mining hepatoma Humans Ions - blood liquid chromatography Liver diseases Liver Diseases - blood Mass Spectrometry metabolites metabolome Metabolomics metrology Mutual information patients Support Vector Machine SVM-RFE
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