A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information
A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information
复制标题
一种基于人工对比变量和互信息的支持向量机-递归特征消除特征选择方法
DOI：
10.1016/j.jchromb.2012.05.020
复制
发表时间：
2012-12-01
影响因子：
3
通讯作者：
Xu, Guowang
中科院分区：
文献类型：
--
作者：
Lin, Xiaohui;Yang, Fufang;Xu, Guowang
在代谢组学研究中，从高维代谢组数据中筛选出具有鉴别能力的代谢物是非常重要的。支持向量机-递归特征消除（SVM-RFE）是一种有效的特征选择技术，在代谢组数据分析中显示出良好的应用前景。SVM-RFE是根据支持向量来度量特征的权重，高维数据中的噪声和非信息变量会影响SVM学习模型的超平面。因此，我们提出了一种互信息（MI）-SVM-RFE方法，通过人工变量和MI过滤掉噪声和非信息变量，然后进行SVM-RFE选择最具鉴别力的特征。采用液相色谱-质谱联用技术（LC-MS）分析慢性乙型肝炎（B）、肝硬化和肝细胞癌患者的血清代谢组学数据集，以验证该方法的有效性。三种肝脏疾病之间的区分准确率为74.33 +/- 2.98%，优于原始SVM-RFE的72.00 +/- 4.15%。定义了34个离子特征以区分对照和3种肝病，其中17种被识别。(C)2012爱思唯尔有限公司版权所有。
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.