A Bayesian hybrid Huberized support vector machine and its applications in high-dimensional medical data

Sounak Chakraborty and Ruixin Guo

Computational Statistics & Data Analysis, 2011, vol. 55, issue 3, 1342-1356

Abstract: A hybrid Huberized support vector machine (HHSVM) with an elastic-net penalty has been developed for cancer tumor classification based on thousands of gene expression measurements. In this paper, we develop a Bayesian formulation of the hybrid Huberized support vector machine for binary classification. For the coefficients of the linear classification boundary, we propose a new type of prior, which can select variables and group them together simultaneously. Our proposed prior is a scale mixture of normal distributions and independent gamma priors on a transformation of the variance of the normal distributions. We establish a direct connection between the Bayesian HHSVM model with our special prior and the standard HHSVM solution with the elastic-net penalty. We propose a hierarchical Bayes technique and an empirical Bayes technique to select the penalty parameter. In the hierarchical Bayes model, the penalty parameter is selected using a beta prior. For the empirical Bayes model, we estimate the penalty parameter by maximizing the marginal likelihood. The proposed model is applied to two simulated data sets and three real-life gene expression microarray data sets. Results suggest that our Bayesian models are highly successful in selecting groups of similarly behaved important genes and predicting the cancer class. Most of the genes selected by our models have shown strong association with well-studied genetic pathways, further validating our claims.

Keywords: Cancer; classification; Elastic-net; penalty; Gene; expression; microarrays; Gene; selection; and; grouping; Hierarchical; and; empirical; Bayes; Hybrid; Huberized; support; vector; machine (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-9473(10)00367-1
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:55:y:2011:i:3:p:1342-1356

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().