Feature selection for microarray data using least squares SVM and particle swarm optimization

E.K. TANG; P.N. SUGANTHAN; X. YAO

doi:10.1109/cibcb.2005.1594892

Feature selection for microarray data using least squares SVM and particle swarm optimization

E.K. TANG, P.N. SUGANTHAN, X. YAO

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

33 Citations (Scopus)

Abstract

Feature selection is an important preprocessing technique for many pattern recognition problems. When the number of features is very large while the number of samples is relatively small as in the micro-array data analysis, feature selection is even more important. This paper proposes a novel feature selection method to perform gene selection from DNA microarray data. The method originates from the least squares support vector machine (LSSVM). The particle swarm optimization (PSO) algorithm is also employed to perform optimization. Experimental results clearly demonstrate good and stable performance of the proposed method. © 2005 IEEE.

Original language	English
Title of host publication	Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05
Publisher	IEEE Computer Society
Number of pages	8
ISBN (Electronic)	9780780393875
ISBN (Print)	0780393872
DOIs	https://doi.org/10.1109/cibcb.2005.1594892
Publication status	Published - 2005
Externally published	Yes
Event	2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2005 - La Jolla, United States Duration: 15 Nov 2005 → 15 Nov 2005

Conference

Conference	2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2005
Country/Territory	United States
City	La Jolla
Period	15/11/05 → 15/11/05

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10.1109/cibcb.2005.1594892

Cite this

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title = "Feature selection for microarray data using least squares SVM and particle swarm optimization",

abstract = "Feature selection is an important preprocessing technique for many pattern recognition problems. When the number of features is very large while the number of samples is relatively small as in the micro-array data analysis, feature selection is even more important. This paper proposes a novel feature selection method to perform gene selection from DNA microarray data. The method originates from the least squares support vector machine (LSSVM). The particle swarm optimization (PSO) algorithm is also employed to perform optimization. Experimental results clearly demonstrate good and stable performance of the proposed method. {\textcopyright} 2005 IEEE.",

author = "E.K. TANG and P.N. SUGANTHAN and X. YAO",

year = "2005",

doi = "10.1109/cibcb.2005.1594892",

language = "English",

isbn = "0780393872",

booktitle = "Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05",

publisher = "IEEE Computer Society",

address = "United States",

note = "2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2005 ; Conference date: 15-11-2005 Through 15-11-2005",

}

TANG, EK, SUGANTHAN, PN & YAO, X 2005, Feature selection for microarray data using least squares SVM and particle swarm optimization. in Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05., 1594892, IEEE Computer Society, 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2005, La Jolla, California, United States, 15/11/05. https://doi.org/10.1109/cibcb.2005.1594892

Feature selection for microarray data using least squares SVM and particle swarm optimization. / TANG, E.K.; SUGANTHAN, P.N.; YAO, X.
Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05. IEEE Computer Society, 2005. 1594892.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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AU - TANG, E.K.

AU - SUGANTHAN, P.N.

AU - YAO, X.

PY - 2005

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N2 - Feature selection is an important preprocessing technique for many pattern recognition problems. When the number of features is very large while the number of samples is relatively small as in the micro-array data analysis, feature selection is even more important. This paper proposes a novel feature selection method to perform gene selection from DNA microarray data. The method originates from the least squares support vector machine (LSSVM). The particle swarm optimization (PSO) algorithm is also employed to perform optimization. Experimental results clearly demonstrate good and stable performance of the proposed method. © 2005 IEEE.

AB - Feature selection is an important preprocessing technique for many pattern recognition problems. When the number of features is very large while the number of samples is relatively small as in the micro-array data analysis, feature selection is even more important. This paper proposes a novel feature selection method to perform gene selection from DNA microarray data. The method originates from the least squares support vector machine (LSSVM). The particle swarm optimization (PSO) algorithm is also employed to perform optimization. Experimental results clearly demonstrate good and stable performance of the proposed method. © 2005 IEEE.

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DO - 10.1109/cibcb.2005.1594892

M3 - Conference paper (refereed)

SN - 0780393872

BT - Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05

PB - IEEE Computer Society

T2 - 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2005

Y2 - 15 November 2005 through 15 November 2005

ER -

Feature selection for microarray data using least squares SVM and particle swarm optimization

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