SMC-PBC-SVM: A Parallel Pmplementation of Support Vector Machines for Data Classification
Document Type
Conference Proceeding
Publication Date
7-16-2012
School
Computing Sciences and Computer Engineering
Abstract
The Support Vector Machine (SVM) is one of the most effective machine learning algorithms for data classification, which have a significant area of research. Since the training process of large datasets is computationally insensitive, there is a need to improve its efficiency using high performance computing techniques. In this paper, we developed an efficient parallel algorithm, SMC-PBC-SVM, which combines a Parallel Binary Class with Serial Multi-Class Support Vector Machines for classification. The SMC-PBC-SVM algorithm was implemented using the object-oriented C++ programming language and standard Message passing Interface (MPI) communication routines. The parallel code was executed on an ALBACORE Linux cluster, and then tested with four datasets with different sizes: Earthworm, Protein, Mnist, and Minst8m. The results show that the SMC-PBC-SVM implementation can significantly improve the performance of data classification without the loss of accuracy. The results also demonstrated a form of proportionality between the size of the dataset and the SMC-PBC-SVM efficiency. As the dataset becomes larger, the SMC-PBC-SVm achieves a higher effieciency.
Publication Title
2012 World Congress in Computer Science, Computer Engineering, and Applied Computing
Recommended Citation
Ahmed, R.,
Ali, A.,
Zhang, C.
(2012). SMC-PBC-SVM: A Parallel Pmplementation of Support Vector Machines for Data Classification. 2012 World Congress in Computer Science, Computer Engineering, and Applied Computing.
Available at: https://aquila.usm.edu/fac_pubs/20537
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