Optimal clustering-based ART1 classification in bioinformatics: g-protein coupled receptors classification

  • Authors:
  • Kyu Cheol Cho;Da Hye Park;Yong Beom Ma;Jong Sik Lee

  • Affiliations:
  • School of Computer Science and Engineering, Inha University, Incheon, South Korea;School of Computer Science and Engineering, Inha University, Incheon, South Korea;School of Computer Science and Engineering, Inha University, Incheon, South Korea;School of Computer Science and Engineering, Inha University, Incheon, South Korea

  • Venue:
  • ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
  • Year:
  • 2006

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Abstract

Protein sequence data have been revealed in current genome research and have been noticed in demand of classifier for new protein classification. This paper proposes the optimal clustering-based ART1 classifier for the GPCR data classification and processes the GPCR data classification. We focuses on a demand of optimal classifier system for protein sequence data classification. The optimal clustering-based ART1 classifier reduces processing cost for classification effectively. We compare classification success rate to those of Backpropagation Neural Network and SVM. In experimental result of the optimal clustering-based ART1 classifier, classification success rate of ClassA group is 99.7% and that of the others group is 96.6%. This result demonstrates that the optimal clustering-based ART1 classifier is useful to the GPCR data classification. The classification processing time of the optimal clustering-based ART1 classifier is the 27% less than that of the Backpropagation Neural Network and is the 39% less than that of the SVM in an optimal clustering rate which is 15%. And the classification processing time of the optimal clustering-based ART1 classifier is the 39% less than that of the optimal clustering-based ART1 classifier in a prediction success rate which is 96%. This result demonstrates that the optimal clustering-based ART1 classifier provides the high performance classification and the low processing cost in the GPCR data classification.