文章基本信息

标题：Gene Expression-Based Glioma Classification Using Hierarchical Bayesian Vector Machines
本地全文：下载
作者：Sounak Chakraborty ; University of Missouri, Columbia ; USA Bani K. Mallick 等
期刊名称：Sankhya. Series A, mathematical statistics and probability
印刷版ISSN：0976-836X
电子版ISSN：0976-8378
出版年度：2007
卷号：69
期号：03
出版社：Indian Statistical Institute
摘要：This paper considers several Bayesian classification methods for the analysis of the glioma cancer with microarray data based on reproducing kernel Hilbert space under the multiclass setup. We consider the multinomial logit likelihood as well as the likelihood related to the multiclass Support Vector Machine (SVM) model. It is shown that our proposed Bayesian classification models with multiple shrinkage parameters can produce more accurate classification scheme for the glioma cancer compared to several existing classical methods. We have also proposed a Bayesian variable selection scheme for selecting the differentially expressed genes integrated with our model. This integrated approach improves classifier design by yielding simultaneous gene selection.
关键词：Gibbs sampling, Markov chain Monte Carlo, Metropolis- Hastings algorithm, microarrays, reproducing kernel Hilbert space, shrinkage parameters, support vector machines.