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  • Title: Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data.
    Author: Zhang C, Li P, Rajendran A, Deng Y, Chen D.
    Journal: BMC Bioinformatics; 2006 Dec 12; 7 Suppl 4(Suppl 4):S15. PubMed ID: 17217507.
    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. CONCLUSION: 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.
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