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Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data
- Source :
- BMC Bioinformatics, BMC Bioinformatics, Vol 7, Iss Suppl 4, p S15 (2006)
- Publication Year :
- 2006
- Publisher :
- BioMed Central, 2006.
-
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.
- Subjects :
- Computer science
Process (engineering)
Data classification
02 engineering and technology
lcsh:Computer applications to medicine. Medical informatics
computer.software_genre
Machine learning
Multicategory
Biochemistry
Computing Methodologies
Pattern Recognition, Automated
03 medical and health sciences
Structural Biology
Artificial Intelligence
Neoplasms
0202 electrical engineering, electronic engineering, information engineering
Biomarkers, Tumor
Cluster Analysis
Humans
Diagnosis, Computer-Assisted
lcsh:QH301-705.5
Molecular Biology
030304 developmental biology
Oligonucleotide Array Sequence Analysis
0303 health sciences
business.industry
Applied Mathematics
Research
Gene Expression Profiling
Computer Science Applications
Neoplasm Proteins
Support vector machine
ComputingMethodologies_PATTERNRECOGNITION
lcsh:Biology (General)
lcsh:R858-859.7
020201 artificial intelligence & image processing
Data mining
Artificial intelligence
business
computer
Algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 14712105
- Volume :
- 7
- Issue :
- Suppl 4
- Database :
- OpenAIRE
- Journal :
- BMC Bioinformatics
- Accession number :
- edsair.doi.dedup.....8c1f4a8d6ed93bb9a611d4d7ef204b81