Adaptive Denoising in Spectral Analysis by Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{rowland:2002:adisabgp,
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author = "Jem J. Rowland and Janet Taylor",
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title = "Adaptive Denoising in Spectral Analysis by Genetic
Programming",
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booktitle = "Proceedings of the 2002 Congress on Evolutionary
Computation CEC2002",
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editor = "David B. Fogel and Mohamed A. El-Sharkawi and
Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and
Mark Shackleton",
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pages = "133--138",
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year = "2002",
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publisher = "IEEE Press",
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publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
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organisation = "IEEE Neural Network Council (NNC), Institution of
Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
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ISBN = "0-7803-7278-6",
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month = "12-17 " # may,
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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)",
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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",
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DOI = "doi:10.1109/CEC.2002.1006222",
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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
Citations