Explanatory analysis of spectroscopic data using machine learning of simple, interpretable rules

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Royston Goodacre",
  title =        "Explanatory analysis of spectroscopic data using
                 machine learning of simple, interpretable rules",
  journal =      "Vibrational Spectroscopy",
  year =         "2003",
  volume =       "32",
  pages =        "33--45",
  number =       "1",
  month =        "5 " # aug,
  note =         "A collection of Papers Presented at Shedding New Light
                 on Disease: Optical Diagnostics for the New Millennium
                 (SPEC 2002) Reims, France 23-27 June 2002",
  keywords =     "genetic algorithms, genetic programming, Artificial
                 neural networks, ANN, FT-IR",
  ISSN =         "0924-2031",
  URL =          "http://www.biospec.net/learning/Metab06/Goodacre-FTIRmaps.pdf",
  URL =          "http://www.sciencedirect.com/science/article/B6THW-48XJP5P-2/2/64840c1f311b856106e124993425ab92",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=",
  DOI =          "doi:10.1016/S0924-2031(03)00045-6",
  language =     "en",
  oai =          "oai:CiteSeerXPSU:",
  abstract =     "Whole organism or tissue profiling by vibrational
                 spectroscopy produces vast amounts of seemingly
                 unintelligible data. However, the characterisation of
                 the biological system under scrutiny is generally
                 possible only in combination with modern supervised
                 machine learning techniques, such as artificial neural
                 networks (ANNs). Nevertheless, the interpretation of
                 the calibration models from ANNs is often very
                 difficult, and the information in terms of which
                 vibrational modes in the infrared or Raman spectra are
                 important is not readily available. ANNs are often
                 perceived as 'black box' approaches to modelling
                 spectra, and to allow the deconvolution of complex
                 hyperspectral data it is necessary to develop a system
                 that itself produces 'rules' that are readily
                 comprehensible. Evolutionary computation, and in
                 particular genetic programming (GP), is an ideal method
                 to achieve this. An example of how GP can be used for
                 Fourier transform infrared (FT-IR) image analysis is
                 presented, and is compared with images produced by
                 principal components analysis (PCA), discriminant
                 function analysis (DFA) and partial least squares (PLS)
  owner =        "wlangdon",

Genetic Programming entries for Royston Goodacre