Name
Title | ||
|---|---|---|
Optimal clustering-based ART1 classification in bioinformatics: g-protein coupled receptors classification |
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11881070_80 | ICNC (1) |
Keywords | Field | DocType |
protein sequence data classification,art1 classifier,backpropagation neural network,optimal clustering rate,optimal classifier system,high performance classification,classification success rate,classification processing time,new protein classification,art1 classification,receptors classification,gpcr data classification,g protein coupled receptor,protein sequence | Correlation clustering,Computer science,Support vector machine,Artificial intelligence,Data classification,Bioinformatics,Margin classifier,Linear classifier,Cluster analysis,Machine learning,Bayes classifier,Quadratic classifier | Conference |
Volume | ISSN | ISBN |
4221 | 0302-9743 | 3-540-45901-4 |
Citations | PageRank | References |
1 | 0.35 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kyu Cheol Cho | 1 | 2 | 2.09 |
| Da Hye Park | 2 | 2 | 1.05 |
| Yong Beom Ma | 3 | 14 | 2.34 |
| Jong Sik Lee | 4 | 74 | 18.95 |