Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis
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- @Article{Winkler:2009:GPEM,
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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",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2009",
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volume = "10",
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number = "2",
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pages = "111--140",
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month = jun,
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keywords = "genetic algorithms, genetic programming,
Adaptation/self-adaptation, Data mining, Classifier
systems, Empirical study, Medicine, SVM",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-008-9076-8",
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abstract = "There are several data based methods in the field of
artificial intelligence which are nowadays frequently
used for analyzing 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 five medical benchmark
classification problems 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 approach presented here is able to
produce comparable or even better results than linear
modeling methods, artificial neural networks, kNN
classification, support vector machines and also
various genetic programming approaches.",
- }
Genetic Programming entries for
Stephan M Winkler
Michael Affenzeller
Stefan Wagner
Citations