Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data.
Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data.
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DOI：
10.1186/1471-2105-7-s4-s15
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发表时间：
2006-12-12
影响因子：
3
通讯作者：
Chen D
中科院分区：
文献类型：
--
作者：
Zhang C;Li P;Rajendran A;Deng Y;Chen D
多类别支持向量机 (MC-SVM) 是功能强大的分类系统，在各种数据分类问题上具有出色的性能。由于在传统的多类别支持向量机中为大型数据集生成模型的过程是计算密集型的，因此需要使用高性能计算技术来提高性能。本文基于支持向量机的顺序最小优化型分解方法（SMO-SVM）开发了并行多类别支持向量机（PMC-SVM）。它使用 MPI 和 C++ 库并行实现，并在共享内存超级计算机和 Linux 集群上执行，用于微阵列数据的多类别分类。使用具有多个诊断类别（例如不同癌症类型和正常组织类型）的四个微阵列数据集对 PMC-SVM 进行了分析和评估。实验表明，与之前的工作相比，PMC-SVM可以在不损失准确性的情况下显着提高微阵列数据的分类性能。
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. 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. 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.