Adaptive Denoising in Spectral Analysis by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Jem J. Rowland and Janet Taylor",
  title =        "Adaptive Denoising in Spectral Analysis by Genetic
  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
  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