Data Fusion by Intelligent Classifier Combination

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "B. F. Buxton and W. B. Langdon and S. J. Barrett",
  title =        "Data Fusion by Intelligent Classifier Combination",
  journal =      "Measurement and Control",
  year =         "2001",
  editor =       "Qing-Ping Yang",
  volume =       "34",
  number =       "8",
  pages =        "229--234",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0020-2940",
  URL =          "",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1177/002029400103400802",
  size =         "6 pages",
  abstract =     "The use of hybrid intelligent systems in industrial
                 and commercial applications is briefly reviewed. The
                 potential for application of such systems, in
                 particular those that combine results from several
                 constituent classifiers, to problems in drug design is
                 discussed. It is shown that, although there are no
                 general rules as to how a number of classifiers should
                 best be combined, effective combinations can
                 automatically be generated by genetic programming (GP).
                 A robust performance measure based on the area under
                 classifier receiver-operating-characteristic (ROC)
                 curves is used as a fitness measure in order to
                 facilitate evolution of multi-classifier systems that
                 outperform their constituent individual classifiers.
                 The approach is illustrated by application to publicly
                 available Landsat data and to pharmaceutical data of
                 the kind used in one stage of the drug design
  notes =        "
                 'Measurement + Control is neither a learned journal nor
                 a commercial trade publication' feature issue of M&C on
                 Signal Processing

                 Awarded best paper prize by the Worshipful Company of
                 Instrument Makers.",

Genetic Programming entries for Bernard Buxton William B Langdon S J Barrett