Variable selection for Fisher linear discriminant analysis using the modified sequential backward selection algorithm for the microarray data
Variable selection for Fisher linear discriminant analysis using the modified sequential backward selection algorithm for the microarray data
复制标题
使用针对微阵列数据的改进的顺序向后选择算法进行 Fisher 线性判别分析的变量选择
DOI：
10.1016/j.amc.2014.03.141
复制
发表时间：
2014-07-01
影响因子：
4
通讯作者：
Liu, Jin-Shan
中科院分区：
文献类型：
--
作者：
Peng, Hong-Yi;Jiang, Chun-Fu;Liu, Jin-Shan
其中一个主要的挑战是小样本量相比，大的特征数量的微阵列数据。变量选择是通过基因表达数据改善癌症诊断或根据表型分类的重要步骤。在这项研究中，我们提出了一个修改的顺序向后选择（SBS）算法来处理的情况下，协方差矩阵是奇异的。在此基础上，提出了一种基于加权马氏距离和改进SBS方法的变量选择算法。在此基础上，提出了一种Fisher线性判别方法，同时考虑了基因的联合判别能力，提高了肿瘤分类的准确性。为了验证该方法的有效性，我们将该方法应用于两个不同的DNA微阵列数据集进行实验研究。实验结果表明，与马尔可夫随机场方法和独立变量组分析I方法相比，该方法对肿瘤分类的效果更好，表明该变量选择方法在考虑肿瘤分类基因的联合判别力的情况下，可以获得更准确、更有信息量的基因子集. (C)2014爱思唯尔公司All rights reserved.
One of the major challenges is small sample size as compared to large features number for microarray data. Variable selection is an important step for improving diagnostics of cancer or the classification according to the phenotypes via gene expression data. In this study, we propose a modified sequential backward selection (SBS) algorithm to deal with the case where the covariance matrix is singular. Then we propose a variable selection algorithm based on the weighted Mahalanobis distance and modified SBS methods. Furthermore, based on the proposed variable selection algorithm, a Fisher linear discriminant method is proposed to improve the accuracy of tumor classification through simultaneously taking into account genes' joint discriminatory power. To validate the efficiency, we apply the proposed discriminant method to two different DNA microarray data sets for experiment investigation. The empirical results show that our method for tumor classification can obtain better classification effectiveness than Markov random field method and independent variable group analysis I methods, which demonstrates that the proposed variable selection method can obtain more correct and informative gene subset if taking into account the joint discriminatory power of genes for tumor classification. (C) 2014 Elsevier Inc. All rights reserved.