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.
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Zhao, XM., Huang, DS., Cheung, Ym., Wang, Hq., Huang, X. (2004). A Novel Hybrid GA/SVM System for Protein Sequences Classification. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_2
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DOI: https://doi.org/10.1007/978-3-540-28651-6_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22881-3
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