Bayesian variable selection in multinomial probit model for classifying high-dimensional data

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• 作者：Aijun Yang ; Yunxian Li ; Niansheng Tang ; Jinguan Lin
• 关键词：Bayesian stochastic search variable selection ; Generalized $$g$$ g ; prior ; Multi ; class classification
• 刊名：Computational Statistics
• 出版年：2015
• 出版时间：June 2015
• 年：2015
• 卷：30
• 期：2
• 页码：399-418
• 全文大小：842 KB
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• 作者单位：Aijun Yang (1) (2)
  Yunxian Li (3) (4)
  Niansheng Tang (4)
  Jinguan Lin (5)
  
  1. College of Economics and Management, Nanjing Forestry University, Nanjing, Jiangsu, China
  2. School of Economics and Management, Southeast University, Nanjing, Jiangsu, China
  3. School of Finance, Yunnan University of Economics and Finance, Kunming, Yunan, China
  4. Department of Statistics, Yunnan University, Kunming, Yunan, China
  5. Department of Mathematics, Southeast University, Nanjing, Jiangsu, China
  
• 刊物类别：Mathematics and Statistics
• 刊物主题：Mathematics
  Statistics
  Statistics
  Probability and Statistics in Computer Science
  Probability Theory and Stochastic Processes
  Economic Theory
  
• 出版者：Physica Verlag, An Imprint of Springer-Verlag GmbH
• ISSN：1613-9658

文摘

Selecting a small number of relevant genes for classification has received a great deal of attention in microarray data analysis. While the development of methods for microarray data with only two classes is relevant, developing more efficient algorithms for classification with any number of classes is important. In this paper, we propose a Bayesian stochastic search variable selection approach for multi-class classification, which can identify relevant genes by assessing sets of genes jointly. We consider a multinomial probit model with a generalized \(g\)-prior for the regression coefficients. An efficient algorithm using simulation-based MCMC methods are developed for simulating parameters from the posterior distribution. This algorithm is robust to the choice of initial value, and produces posterior probabilities of relevant genes for biological interpretation. We demonstrate the performance of the approach with two well-known gene expression profiling data: leukemia data, lymphoma data, SRBCTs data and NCI60 data. Compared with other classification approaches, our approach selects smaller numbers of relevant genes and obtains competitive classification accuracy based on obtained results.