Academic Journals Database
Disseminating quality controlled scientific knowledge

Gene selection algorithm by combining reliefF and mRMR

Author(s): Zhang Yi | Ding Chris | Li Tao

Journal: BMC Genomics
ISSN 1471-2164

Volume: 9;
Issue: Suppl 2;
Start page: S27;
Date: 2008;
VIEW PDF   PDF DOWNLOAD PDF   Download PDF Original page

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.

HR software für Hotellerie

Automatische Erstellung
von Personaldokumente
und Anmeldungen bei Behörden


Tango Rapperswil
Tango Rapperswil