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This article is part of the supplement: Third Annual MCBIOS Conference. Bioinformatics: A Calculated Discovery

Open Access Proceedings

Improving the Performance of SVM-RFE to Select Genes in Microarray Data

Yuanyuan Ding and Dawn Wilkins

Author Affiliations

Computer & Information Science Department, The University of Mississippi, University, MS, USA

BMC Bioinformatics 2006, 7(Suppl 2):S12  doi:10.1186/1471-2105-7-S2-S12

Published: 26 September 2006

Abstract

Background

Recursive Feature Elimination is a common and well-studied method for reducing the number of attributes used for further analysis or development of prediction models. The effectiveness of the RFE algorithm is generally considered excellent, but the primary obstacle in using it is the amount of computational power required.

Results

Here we introduce a variant of RFE which employs ideas from simulated annealing. The goal of the algorithm is to improve the computational performance of recursive feature elimination by eliminating chunks of features at a time with as little effect on the quality of the reduced feature set as possible. The algorithm has been tested on several large gene expression data sets. The RFE algorithm is implemented using a Support Vector Machine to assist in identifying the least useful gene(s) to eliminate.

Conclusion

The algorithm is simple and efficient and generates a set of attributes that is very similar to the set produced by RFE.