Document Type
Article
Publication Date
2006
Department
Computing
School
Computing Sciences and Computer Engineering
Abstract
Background: 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.
Results: 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.
Conclusions: 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.
Publication Title
BMC Bioinformatics
Volume
7
Issue
S4
First Page
S15
Recommended Citation
Zhang, C.,
Li, P.,
Rajendran, A.,
Deng, Y.,
Chen, D.
(2006). Parallelization of Multicategory Support Vector Machines (PMC- SVM) for Classifying Microarray Data. BMC Bioinformatics, 7(S4), S15.
Available at: https://aquila.usm.edu/fac_pubs/2518
Comments
Published by 'BMC Bioinformatics' at 10.1186/1471-2105-7-S4-S15.