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Parallel Multicategory Support Vector Machines (PMC-SVM) for Classifying Microcarray Data

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Parallel Multicategory Support Vector Machines (PMC-SVM) for Classifying Microcarray Data
Multicategory Support Vector Machines (MC-SVM) are powerful classification systems with excellent performance in a variety of biological classification problems. However, the process of generating models in traditional multicategory support vector machines is very time-consuming, especially for large datasets. In this paper, parallel multicategory support vector machines (PMC-SVM) have been developed based on the sequential minimum optimization-type decomposition methods for support vector machines (SMO-SVM). It was implemented in parallel using MPI and C++ on both shared memory supercomputer and Linux clusters, and used for multicategory classification. The performance of PMCSVM has been analyzed and evaluated using several datasets including two microarray datasets with totally 31 diagnostic categories, 25 cancer types and 12 normal tissue types. The experiments show that the PMC-SVM can significantly improve the performance of classification without loss of accuracy, compared with ...
Chaoyang Zhang, Peng Li, Arun Rajendran, Youping D
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where IMSCCS
Authors Chaoyang Zhang, Peng Li, Arun Rajendran, Youping Deng
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