Structural Risk Minimisation based gene expression profiling analysis

Xue Wen Chen*, Byron Gerlach, Dechang Chen, Zhenqiu Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)153-169
Number of pages17
JournalInternational Journal of Bioinformatics Research and Applications
Issue number2
StatePublished - 2007
Externally publishedYes


  • Bioinformatics
  • Biomarker discovery
  • Cancer classification
  • GA
  • Gene expression analysis
  • Genetic algorithm
  • Machine learning
  • Microarray
  • Multi-class feature selection
  • Overfitting
  • SRM
  • Structural Risk Minimisation


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