A Hybrid Computational Intelligence Approach Combining Genetic Programming and Heuristic Classification for Pap-Smear Diagnosis

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Tsakonas:2001:EUNITEpap,
  title =        "A Hybrid Computational Intelligence Approach Combining
                 Genetic Programming and Heuristic Classification for
                 Pap-Smear Diagnosis",
  author =       "Athanasios Tsakonas and Georgios D. Dounias and 
                 Jan Jantzen and Beth Bjerregaard",
  booktitle =    "Proceedings of the CD-rom EUNITE-01, European
                 Symposium on Intelligent Technologies, Hybrid Systems
                 and their Implementation on Smart Adaptive Systems
                 Verlag-Mainz",
  year =         "2001",
  pages =        "516--515",
  address =      "Tenerife, Spain",
  keywords =     "genetic algorithms, genetic programming, hybrid
                 computational intelligence, medical diagnosis,
                 pap-smear test, heuristic classification, evolutionary
                 computation, intelligent systems",
  URL =          "http://www.eunite.org/eunite/events/eunite2001/Papers/13354_P_Dounias.pdf",
  size =         "10 pages",
  language =     "en",
  abstract =     "The paper suggests the combined use of different
                 computational intelligence (CI) techniques in a hybrid
                 scheme, as an effective approach to medical diagnosis.
                 Getting to know the advantages and disadvantages of
                 each computational intelligence technique in the recent
                 years, the time has come for proposing successful
                 combinations of CI tools and techniques for the
                 improvement of decision making, diagnosis and
                 classification in complex domains of application. In
                 the current approach genetic programming is embedded
                 within a heuristic scheme for classification of medical
                 records into different diagnoses. The final result is a
                 short but robust rule based classification scheme,
                 achieving high degree of classification accuracy
                 (exceeding 90percent of accuracy for most classes) in a
                 meaningful and user-friendly representation form for
                 the medical expert. The domain of application analysed
                 through the paper is the well-known Pap-Test problem,
                 corresponding to a numerical database, which consists
                 of 450 medical records, 25 diagnostic attributes and 5
                 different diagnostic classes. Experimental data are
                 divided in two equal parts for the training and testing
                 phase, and 8 mutually dependent rules for diagnosis are
                 generated. Medical experts comment on the nature, the
                 meaning and the usability of the acquired results.",
  notes =        "http://www.elite-foundation.org/ELITE/programme%202001.htm",
}

Genetic Programming entries for Athanasios D Tsakonas Georgios Dounias Jan Jantzen Beth Bjerregaard

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