Adaptive Denoising in Spectral Analysis by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{rowland:2002:adisabgp,

• author = "Jem J. Rowland and Janet Taylor",
• title = "Adaptive Denoising in Spectral Analysis by Genetic Programming",
• booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002",
• editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton",
• pages = "133--138",
• year = "2002",
• publisher = "IEEE Press",
• publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
• organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)",
• ISBN = "0-7803-7278-6",
• month = "12-17 " # may,
• notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)",
• keywords = "genetic algorithms, genetic programming, adaptive denoising, evolutionary search, predictive power, spectral analysis, spectral resolution, supervised interpretation, infrared spectra, infrared spectroscopy, spectral analysis, spectroscopy computing",
• DOI = "doi:10.1109/CEC.2002.1006222",
• abstract = "This paper relates to supervised interpretation of the infrared analytical spectra of complex biological samples. The aim is to produce a model that can predict the value of a measurand of interest, such as the concentration of a particular chemical constituent in complex biological material. Conventionally, a number of spectra are co-added to reduce measurement noise and this is time consuming. In this paper we demonstrate the ability of evolutionary search to provide adaptive averaging of spectral regions to provide selective tradeoff between spectral resolution and signal-to-noise ratio. The resultant denoised subset of the variables is then input to a proprietary Genetic Programming (GP) package which forms a predictive model that compares well in predictive power with a combination of Partial Least Squares Regression (PLS) and adaptive denoising. This demonstrates the considerable advantage that, given appropriate node functions, the GP could handle the entire process of denoising and forming the final predictive model all in one stage. This reduces or removes the need for co-adding with a consequent reduction in data acquisition time",

}

Genetic Programming entries for Jem J Rowland Janet Taylor

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