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:1.7964
- @InProceedings{Winkler:2006:GECCOWKS,
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author = "Stephan M. Winkler and Michael Affenzeller and
Stefan Wagner",
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title = "Using Enhanced Genetic Programming Techniques for
Evolving Classifiers in the Context of Medical
Diagnosis - An Empirical Study",
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booktitle = "MedGEC 2006 GECCO Workshop on Medical Applications of
Genetic and Evolutionary Computation",
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year = "2006",
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editor = "Stephen L Smith and Stefano Cagnoni and
Jano {van Hemert}",
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address = "Seattle, WA, USA",
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month = "8 " # jul,
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keywords = "genetic algorithms, genetic programming.
Adaptation/Self-Adaptation, Classifier Systems,
Empirical Study, Medicine",
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URL = "http://gpbib.cs.ucl.ac.uk/gecco2006etc/papers/wksp115.pdf",
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size = "8 pages",
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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.",
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notes = "GECCO-2006WKS Distributed on CD-ROM at the GECCO 2006
conference",
- }
Genetic Programming entries for
Stephan M Winkler
Michael Affenzeller
Stefan Wagner
Citations