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This article is part of the supplement: Symposium of Computations in Bioinformatics and Bioscience (SCBB06)

Open Access Research

Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data

Chaoyang Zhang1*, Peng Li1, Arun Rajendran1, Youping Deng2* and Dequan Chen3

Author Affiliations

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

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BMC Bioinformatics 2006, 7(Suppl 4):S15  doi:10.1186/1471-2105-7-S4-S15

Published: 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.