Structural Risk Minimisation based gene expression profiling analysis
by Xue-wen Chen, Byron Gerlach, Dechang Chen, Zhenqiu Liu
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 3, No. 2, 2007

Abstract: For microarray based cancer classification, feature selection is a common method for improving classifier generalisation. Most wrapper methods use cross validation methods to evaluate feature sets. For small sample problems like microarray, however, cross validation methods may overfit the data. In this paper, we propose a Structural Risk Minimisation (SRM) based method for gene selection in cancer classification. SRM principle allows for reducing the probable bound on generalisation error and thus avoids overfitting problems. The experimental results show that the proposed method produces significantly better performance than general wrapper methods that use cross validations.

Online publication date: Wed, 09-May-2007

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