Author(s): Zhang Yi | Ding Chris | Li Tao
Journal: BMC Genomics
ISSN 1471-2164
Volume: 9;
Issue: Suppl 2;
Start page: S27;
Date: 2008;
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Original page
ABSTRACT
Abstract Background 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. Results We 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. And we also provide all source codes and datasets for sharing with others. Conclusion The experimental results show that the mRMR-ReliefF gene selection algorithm is very effective.
Journal: BMC Genomics
ISSN 1471-2164
Volume: 9;
Issue: Suppl 2;
Start page: S27;
Date: 2008;
VIEW PDF
DOWNLOAD PDF Original pageABSTRACT
Abstract Background 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. Results We 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. And we also provide all source codes and datasets for sharing with others. Conclusion The experimental results show that the mRMR-ReliefF gene selection algorithm is very effective.
