A Novel Hybrid GA/SVM System for Protein Sequences Classification

Xing Ming Zhao; De Shuang Huang; Yiu Ming Cheung; Hong Qiang Wang; Xin Huang

doi:10.1007/978-3-540-28651-6_2

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

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

25 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 language	English
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	https://doi.org/10.1007/978-3-540-28651-6_2
Publication status	Published - 2004
Event	5th International Conference on Intelligent Data Engineering and Automated Learning - Exeter, United Kingdom Duration: 25 Aug 2004 → 27 Aug 2004

Publication series

Name	Lecture Notes in Computer Science
Volume	3177
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Conference on Intelligent Data Engineering and Automated Learning
Abbreviated title	IDEAL 2004
Country/Territory	United Kingdom
City	Exeter
Period	25/08/04 → 27/08/04

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

Access to Document

10.1007/978-3-540-28651-6_2

Cite this

Zhao, X. M., Huang, D. S., Cheung, Y. M., Wang, H. Q., & Huang, X. (2004). A Novel Hybrid GA/SVM System for Protein Sequences Classification. In Z. R. Yang, R. Everson, & H. Yin (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2004: 5th International Conference, Exeter, UK. August 25-27, 2004. Proceedings (1st ed., pp. 11-16). (Lecture Notes in Computer Science; Vol. 3177). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_2

Zhao, Xing Ming ; Huang, De Shuang ; Cheung, Yiu Ming et al. / A Novel Hybrid GA/SVM System for Protein Sequences Classification. Intelligent Data Engineering and Automated Learning – IDEAL 2004: 5th International Conference, Exeter, UK. August 25-27, 2004. Proceedings. editor / Zheng Rong Yang ; Richard Everson ; Hujun Yin. 1st. ed. Springer Berlin Heidelberg, 2004. pp. 11-16 (Lecture Notes in Computer Science).

@inproceedings{58ac7627f60f4be393ff2433960eb359,

title = "A Novel Hybrid GA/SVM System for Protein Sequences Classification",

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.",

keywords = "Support Vector Machine, Feature Selection, Recognition Rate, Support Vector Machine Classifier, Feature Subset",

author = "Zhao, {Xing Ming} and Huang, {De Shuang} and Cheung, {Yiu Ming} and Wang, {Hong Qiang} and Xin Huang",

note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004 ; Conference date: 25-08-2004 Through 27-08-2004",

year = "2004",

doi = "10.1007/978-3-540-28651-6_2",

language = "English",

isbn = "9783540228813",

series = "Lecture Notes in Computer Science",

publisher = "Springer Berlin Heidelberg",

pages = "11--16",

editor = "Yang, {Zheng Rong} and Richard Everson and Hujun Yin",

booktitle = "Intelligent Data Engineering and Automated Learning – IDEAL 2004",

edition = "1st",

}

Zhao, XM, Huang, DS, Cheung, YM, Wang, HQ & Huang, X 2004, A Novel Hybrid GA/SVM System for Protein Sequences Classification. in ZR Yang, R Everson & H Yin (eds), Intelligent Data Engineering and Automated Learning – IDEAL 2004: 5th International Conference, Exeter, UK. August 25-27, 2004. Proceedings. 1st edn, Lecture Notes in Computer Science, vol. 3177, Springer Berlin Heidelberg, pp. 11-16, 5th International Conference on Intelligent Data Engineering and Automated Learning, Exeter, United Kingdom, 25/08/04. https://doi.org/10.1007/978-3-540-28651-6_2

A Novel Hybrid GA/SVM System for Protein Sequences Classification. / Zhao, Xing Ming; Huang, De Shuang; Cheung, Yiu Ming et al.
Intelligent Data Engineering and Automated Learning – IDEAL 2004: 5th International Conference, Exeter, UK. August 25-27, 2004. Proceedings. ed. / Zheng Rong Yang; Richard Everson; Hujun Yin. 1st. ed. Springer Berlin Heidelberg, 2004. p. 11-16 (Lecture Notes in Computer Science; Vol. 3177).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - A Novel Hybrid GA/SVM System for Protein Sequences Classification

AU - Zhao, Xing Ming

AU - Huang, De Shuang

AU - Cheung, Yiu Ming

AU - Wang, Hong Qiang

AU - Huang, Xin

PY - 2004

Y1 - 2004

N2 - 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.

AB - 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.

KW - Support Vector Machine

KW - Feature Selection

KW - Recognition Rate

KW - Support Vector Machine Classifier

KW - Feature Subset

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U2 - 10.1007/978-3-540-28651-6_2

DO - 10.1007/978-3-540-28651-6_2

M3 - Conference proceeding

AN - SCOPUS:26944485673

SN - 9783540228813

T3 - Lecture Notes in Computer Science

SP - 11

EP - 16

BT - Intelligent Data Engineering and Automated Learning – IDEAL 2004

A2 - Yang, Zheng Rong

A2 - Everson, Richard

A2 - Yin, Hujun

PB - Springer Berlin Heidelberg

T2 - 5th International Conference on Intelligent Data Engineering and Automated Learning

Y2 - 25 August 2004 through 27 August 2004

ER -

Zhao XM, Huang DS, Cheung YM, Wang HQ, Huang X. A Novel Hybrid GA/SVM System for Protein Sequences Classification. In Yang ZR, Everson R, Yin H, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2004: 5th International Conference, Exeter, UK. August 25-27, 2004. Proceedings. 1st ed. Springer Berlin Heidelberg. 2004. p. 11-16. (Lecture Notes in Computer Science). doi: 10.1007/978-3-540-28651-6_2

A Novel Hybrid GA/SVM System for Protein Sequences Classification

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