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 ProceedingsConference paper (refereed)Researchpeer-review

32 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 languageEnglish
Title of host publicationProceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05
PublisherIEEE Computer Society
Number of pages8
ISBN (Print)9780780393875
DOIs
Publication statusPublished - 2005
Externally publishedYes
Event2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2005 - La Jolla, United States
Duration: 15 Nov 200515 Nov 2005

Conference

Conference2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2005
Country/TerritoryUnited States
CityLa Jolla
Period15/11/0515/11/05

Fingerprint

Dive into the research topics of 'Feature selection for microarray data using least squares SVM and particle swarm optimization'. Together they form a unique fingerprint.

Cite this