Gene Expression-Based Glioma Classification Using Hierarchical Bayesian Vector Machines
Abstract
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.
Part of
Citation
Sankhya : The Indian Journal of Statistics, Volume 69, Part 3, pp. 514-547.