Novel feature selection method for genetic programming using metabolomic 1H NMR data

Adrian Charlton; Richard Davis; Sarah Oehlschlager; Julie C. Wilson

doi:doi:10.1016/j.chemolab.2005.09.006

Novel feature selection method for genetic programming using metabolomic 1H NMR data

Adrian Charlton, Richard Davis, Sarah Oehlschlager, Julie C. Wilson

Research output: Contribution to journal › Article › peer-review

Abstract

A novel technique for multivariate data analysis using a two-stage genetic programming (GP) routine for feature selection is described. The method is compared with conventional genetic programming for the classification of genetically modified barley. Metabolic fingerprinting by 1H NMR spectroscopy was used to analyse the differences between transgenic and null-segregant plants. We show that the method has a number of major advantages over standard genetic programming techniques. By selecting a minimal set of characteristic features in the data, the method provides models that are easier to interpret. Moreover the new method achieves better classification results and convergence is reached significantly faster.

Original language	English
Pages (from-to)	50-59
Number of pages	10
Journal	J. Chemom. Intell. Lab. Syst.
Volume	81
Issue number	1
DOIs	https://doi.org/doi:10.1016/j.chemolab.2005.09.006
Publication status	Published - 1 Feb 2006

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doi:10.1016/j.chemolab.2005.09.006

http://www.sciencedirect.com/science/article/pii/S0169743905001498

Cite this

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title = "Novel feature selection method for genetic programming using metabolomic 1H NMR data",

abstract = "A novel technique for multivariate data analysis using a two-stage genetic programming (GP) routine for feature selection is described. The method is compared with conventional genetic programming for the classification of genetically modified barley. Metabolic fingerprinting by 1H NMR spectroscopy was used to analyse the differences between transgenic and null-segregant plants. We show that the method has a number of major advantages over standard genetic programming techniques. By selecting a minimal set of characteristic features in the data, the method provides models that are easier to interpret. Moreover the new method achieves better classification results and convergence is reached significantly faster.",

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