A Novel Hybrid GA/SVM System for Protein Sequences Classification

Xing Ming Zhao*, De Shuang Huang, Yiu Ming CHEUNG, Hong Qiang Wang, Xin Huang

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

21 Citations (Scopus)

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 languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2004
Subtitle of host publication5th International Conference, Exeter, UK. August 25-27, 2004. Proceedings
EditorsZheng Rong Yang, Richard Everson, Hujun Yin
PublisherSpringer Berlin Heidelberg
Pages11-16
Number of pages6
Edition1st
ISBN (Electronic)9783540286516
ISBN (Print)9783540228813
DOIs
Publication statusPublished - 2004
Event5th International Conference on Intelligent Data Engineering and Automated Learning - Exeter, United Kingdom
Duration: 25 Aug 200427 Aug 2004

Publication series

NameLecture Notes in Computer Science
Volume3177
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Conference on Intelligent Data Engineering and Automated Learning
Abbreviated titleIDEAL 2004
Country/TerritoryUnited Kingdom
CityExeter
Period25/08/0427/08/04

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Support Vector Machine
  • Feature Selection
  • Recognition Rate
  • Support Vector Machine Classifier
  • Feature Subset

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