Sparse Bayesian variable selection in multinomial probit regression model with application to high-dimensional data classification

Yang Aijun, Jiang Xuejun, Xiang Liming and Lin Jinguan

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 12, 6137-6150

Abstract: Here we consider a multinomial probit regression model where the number of variables substantially exceeds the sample size and only a subset of the available variables is associated with the response. Thus selecting a small number of relevant variables for classification has received a great deal of attention. Generally when the number of variables is substantial, sparsity-enforcing priors for the regression coefficients are called for on grounds of predictive generalization and computational ease. In this paper, we propose a sparse Bayesian variable selection method in multinomial probit regression model for multi-class classification. The performance of our proposed method is demonstrated with one simulated data and three well-known gene expression profiling data: breast cancer data, leukemia data, and small round blue-cell tumors. The results show that compared with other methods, our method is able to select the relevant variables and can obtain competitive classification accuracy with a small subset of relevant genes.

Date: 2017
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DOI: 10.1080/03610926.2015.1122056

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