Using Enhanced Genetic Programming Techniques for Evolving Classifiers in the Context of Medical Diagnosis - An Empirical Study

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

@InProceedings{Winkler:2006:GECCOWKS,
  author =       "Stephan M. Winkler and Michael Affenzeller and 
                 Stefan Wagner",
  title =        "Using Enhanced Genetic Programming Techniques for
                 Evolving Classifiers in the Context of Medical
                 Diagnosis - An Empirical Study",
  booktitle =    "MedGEC 2006 GECCO Workshop on Medical Applications of
                 Genetic and Evolutionary Computation",
  year =         "2006",
  editor =       "Stephen L Smith and Stefano Cagnoni and 
                 Jano {van Hemert}",
  address =      "Seattle, WA, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming.
                 Adaptation/Self-Adaptation, Classifier Systems,
                 Empirical Study, Medicine",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006etc/papers/wksp115.pdf",
  size =         "8 pages",
  abstract =     "There are several data based methods in the field of
                 artificial intelligence which are nowadays frequently
                 used for analysing classification problems in the
                 context of medical applications. As we show in this
                 paper, the application of enhanced evolutionary
                 computation techniques to classification problems has
                 the potential to evolve classifiers of even higher
                 quality than those trained by standard machine learning
                 methods. On the basis of three medical benchmark
                 classification problems, namely the Wisconsin and the
                 Thyroid data sets taken from the UCI repository as well
                 as the Melanoma data set prepared by members of the
                 Department of Dermatology of the Medical University
                 Vienna, we document that the enhanced genetic
                 programming based approach presented here is able to
                 produce better results than linear modelling methods,
                 artificial neural networks, kNN classification and also
                 standard genetic programming approaches.",
  notes =        "GECCO-2006WKS Distributed on CD-ROM at the GECCO 2006
                 conference",
}

Genetic Programming entries for Stephan M Winkler Michael Affenzeller Stefan Wagner

Citations