This article is part of the supplement: Symposium of Computations in Bioinformatics and Bioscience (SCBB06)
Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data
1 School of Computing, University of Southern Mississippi, Hattiesburg, MS 39406, USA
2 Department of Biological Sciences, University of Southern Mississippi, Hattiesburg, MS 39406, USA
3 Dequan Chen, Institute for Retina Research, Dallas, TX 75231, USA
BMC Bioinformatics 2006, 7(Suppl 4):S15 doi:10.1186/1471-2105-7-S4-S15Published: 12 December 2006
Multicategory Support Vector Machines (MC-SVM) are powerful classification systems with excellent performance in a variety of data classification problems. Since the process of generating models in traditional multicategory support vector machines for large datasets is very computationally intensive, there is a need to improve the performance using high performance computing techniques.
In this paper, Parallel Multicategory Support Vector Machines (PMC-SVM) have been developed based on the sequential minimum optimization-type decomposition method for support vector machines (SMO-SVM). It was implemented in parallel using MPI and C++ libraries and executed on both shared memory supercomputer and Linux cluster for multicategory classification of microarray data. PMC-SVM has been analyzed and evaluated using four microarray datasets with multiple diagnostic categories, such as different cancer types and normal tissue types.
The experiments show that the PMC-SVM can significantly improve the performance of classification of microarray data without loss of accuracy, compared with previous work.