Journal
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
Volume 81, Issue 1, Pages 50-59Publisher
ELSEVIER
DOI: 10.1016/j.chemolab.2005.09.006
Keywords
metabolomics; multivariate data analysis; genetic programming; feature selection; NMR
Ask authors/readers for more resources
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 H-1 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. (c) 2005 Elsevier B.V All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available