Advances in applying genetic programming to machine learning, focussing on classification problems
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- @InProceedings{Winkler:2006:IPDPS,
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author = "S. M. Winkler and M. Affenzeller and S. Wagner",
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title = "Advances in applying genetic programming to machine
learning, focussing on classification problems",
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booktitle = "20th International Parallel and Distributed Processing
Symposium, IPDPS 2006",
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year = "2006",
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pages = "8 pp.?",
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address = "Rhodes Island",
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month = "25-29 " # apr # " 2006",
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "1-4244-0054-6",
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DOI = "doi:10.1109/IPDPS.2006.1639524",
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abstract = "A genetic programming based approach for solving
classification problems is presented in this paper.
Classification is understood as the act of placing an
object into a set of categories, based on the object's
properties; classification algorithms are designed to
learn a function which maps a vector of object features
into one of several classes. This is done by analysing
a set of input-output examples (training samples) of
the function. Here we present a method based on the
theory of genetic algorithms and genetic programming
that interprets classification problems as optimisation
problems: Each presented instance of the classification
problem is interpreted as an instance of an
optimization problem, and a solution is found by a
heuristic optimisation algorithm. The major new aspects
presented in this paper are suitable genetic operators
for this problem class (mainly the creation of new
hypotheses by merging already existing ones and their
detailed evaluation) we have designed and implemented.
The experimental part of the paper documents the
results produced using new hybrid variants of genetic
algorithms as well as investigated parameter
settings.",
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notes = "CD-ROM
Dept. of Software Eng., Upper Austrian Univ. of Appl.
Sci., Hagenberg, Austria",
- }
Genetic Programming entries for
Stephan M Winkler
Michael Affenzeller
Stefan Wagner
Citations