Name
Title | ||
|---|---|---|
Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data. |
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1186/1471-2105-7-S4-S15 | BMC Bioinformatics |
Keywords | DocType | Volume |
support vector machine,algorithms,microarrays,decomposition method,shared memory,linux cluster,microarray data,classification system,bioinformatics | Journal | 7 |
Issue | ISSN | Citations |
S-4 | 1471-2105 | 27 |
PageRank | References | Authors |
0.62 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chaoyang Zhang | 1 | 230 | 22.23 |
| Peng Li | 2 | 75 | 3.20 |
| Arun Rajendran | 3 | 30 | 1.37 |
| Youping Deng | 4 | 631 | 38.43 |
| Dequan Chen | 5 | 27 | 0.96 |