Application of Genetic Programming to Induction of Linear Classification Trees
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{bot:1999:GPilct,
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author = "Martijn Bot and William B. Langdon",
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title = "Application of Genetic Programming to Induction of
Linear Classification Trees",
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booktitle = "Proceedings of the Eleventh Belgium/Netherlands
Conference on Artificial Intelligence (BNAIC'99)",
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year = "1999",
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editor = "Eric Postma and Marc Gyssens",
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pages = "107--114",
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address = "Kasteel Vaeshartelt, Maastricht, Holland",
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month = "3-4 " # nov,
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organisation = "BNVKI, Dutch and the Belgian AI Association",
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keywords = "genetic algorithms, genetic programming, data mining",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/martijn/BNAIC99.bot.18aug99.ps.gz",
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size = "8 pages",
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abstract = "A common problem in datamining is to find accurate
classifiers for a dataset. For this purpose, genetic
programming (GP) is applied to a benchmark of
classification problems. In particular, using GP we are
able to induce decision trees with a linear combination
of variables in each function node. The effects of
techniques as limited error fitness, fitness sharing
Pareto scoring and domination Pareto scoring are
evaluated. Results indicate that GP can be applied
successfully to classification problems. Comparisons
with current state-of-the-art algorithms in machine
learning are presented and areas of future research are
identified.",
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notes = "http://www.bnvki.org/
https://documentserver.uhasselt.be/handle/1942/4193
July 2024 broken http://www.cs.unimaas.nl/~bnvki/",
- }
Genetic Programming entries for
Martijn C J Bot
William B Langdon
Citations