Optimal Clustering-Based ART1 Classification in Bioinformatics: G-Protein Coupled Receptors Classification

  • Kyu Cheol Cho
  • Da Hye Park
  • Yong Beom Ma
  • Jong Sik Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


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.


Optimal Cluster Classification Success Cluster Rate ART1 Classifier ART1 Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kyu Cheol Cho
    • 1
  • Da Hye Park
    • 1
  • Yong Beom Ma
    • 1
  • Jong Sik Lee
    • 1
  1. 1.School of Computer Science and EngineeringInha UniversityIncheonSouth Korea

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