Bioinformatics BI_N0008

title:Optimal Clustering-Based ART1 Classification in Bioinformatics: G-Protein Coupled Receptors Classification
author:Kyu Cheol Cho, Da Hye Park, Yong Beom Ma and Jong Sik Lee
year:2006
place of publish:Springer Berlin / Heidelberg
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.