Abstract
A novel hybrid genetic algorithm(GA)/Support Vector Machine (SVM) system, which selects features from the protein sequences and trains the SVM classifier simultaneously using a multi-objective genetic algorithm, is proposed in this paper. The system is then applied to classify protein sequences obtained from the Protein Information Resource (PIR) protein database. Finally, experimental results over six protein superfamilies are reported, where it is shown that the proposed hybrid GA/SVM system outperforms BLAST and HMMer.
Original language | English |
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Title of host publication | Intelligent Data Engineering and Automated Learning – IDEAL 2004 |
Subtitle of host publication | 5th International Conference, Exeter, UK. August 25-27, 2004. Proceedings |
Editors | Zheng Rong Yang, Richard Everson, Hujun Yin |
Publisher | Springer Berlin Heidelberg |
Pages | 11-16 |
Number of pages | 6 |
Edition | 1st |
ISBN (Electronic) | 9783540286516 |
ISBN (Print) | 9783540228813 |
DOIs | |
Publication status | Published - 2004 |
Event | 5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004 - Exeter, United Kingdom Duration: 25 Aug 2004 → 27 Aug 2004 https://link.springer.com/book/10.1007/b99975 |
Publication series
Name | Lecture Notes in Computer Science |
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Volume | 3177 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004 |
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Abbreviated title | IDEAL 2004 |
Country/Territory | United Kingdom |
City | Exeter |
Period | 25/08/04 → 27/08/04 |
Internet address |
Scopus Subject Areas
- Theoretical Computer Science
- General Computer Science
User-Defined Keywords
- Support Vector Machine
- Feature Selection
- Recognition Rate
- Support Vector Machine Classifier
- Feature Subset