DocumentCode :
1988904
Title :
A Two-Stage Gene Selection Algorithm by Combining ReliefF and mRMR
Author :
Zhang, Yi ; Ding, Chris ; Li, Tao
Author_Institution :
Florida Int. Univ., Miami
fYear :
2007
fDate :
14-17 Oct. 2007
Firstpage :
164
Lastpage :
171
Abstract :
Gene expression data usually contains a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best discriminate biological samples of different types. In this paper, we present a two-stage selection algorithm by combining ReliefF and mRMR: In the first stage, ReliefF is applied to find a candidate gene set; In the second stage, mRMR method is applied to directly and explicitly reduce redundancy for selecting a compact yet effective gene subset from the candidate set. We also perform comprehensive experiments to compare the mRMR-ReliefF selection algorithm with ReliefF, mRMR and other feature selection methods using two classifiers as SVM and Naive Bayes, on seven different datasets. The experimental results show that the mRMR-ReliefF gene selection algorithm is very effective.
Keywords :
Bayes methods; biology computing; cellular biophysics; genetics; molecular biophysics; support vector machines; ReliefF; SVM classifier; feature selection; gene expression; mRMR; naive Bayes classifier; two-stage gene selection algorithm; Biology computing; Computer science; DNA; Data engineering; Diversity reception; Gene expression; Proteins; Sequences; Support vector machine classification; Support vector machines; Gene selection algorithms; mRMR; mRMR-reliefF; reliefF;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Bioengineering, 2007. BIBE 2007. Proceedings of the 7th IEEE International Conference on
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-1509-0
Type :
conf
DOI :
10.1109/BIBE.2007.4375560
Filename :
4375560
Link To Document :
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