The binary Multi-SVM voting system for protein subcellular localization prediction

Authors:
Bo Jin;Yuchun Tang;Yan-Qing Zhang;Chung-Dar Lu;Irene Weber
Affiliations:
Department of Computer Science, Georgia State University, Atlanta, GA;Department of Computer Science, Georgia State University, Atlanta, GA;Department of Computer Science, Georgia State University, Atlanta, GA;Department of Biology, Georgia State University, Atlanta, GA;Department of Biology, Georgia State University, Atlanta, GA
Venue:
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part III
Year:
2005

Citing 5
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Abstract

Support Vector Machine (SVM) as a learning system has been widely employed for pattern recognition and data classification tasks such as biological data classification. Choosing appropriate parameters are essential for SVM to achieve a high global performance. In this paper, we propose a new binary multi-SVM voting system without difficult parameter selection for protein subcellular localization prediction. The sufficient experimental results demonstrate that the multi-SVM voting system can achieve higher average prediction accuracies for the protein subcellular localization prediction than the traditional single-SVM system.