Inducing Diverse Decision Forests with Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{eurogp:SuchyK05,
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author = "Jan Suchy and Jiri Kubalik",
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editor = "Maarten Keijzer and Andrea Tettamanzi and
Pierre Collet and Jano I. {van Hemert} and Marco Tomassini",
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title = "Inducing Diverse Decision Forests with Genetic
Programming",
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booktitle = "Proceedings of the 8th European Conference on Genetic
Programming",
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publisher = "Springer",
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series = "Lecture Notes in Computer Science",
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volume = "3447",
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year = "2005",
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address = "Lausanne, Switzerland",
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month = "30 " # mar # " - 1 " # apr,
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organisation = "EvoNet",
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keywords = "genetic algorithms, genetic programming: Poster",
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ISBN = "3-540-25436-6",
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pages = "301--310",
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URL = "http://ida.felk.cvut.cz/cgi-bin/docarc/public.pl/document/6/suchy05eurogp.pdf",
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DOI = "doi:10.1007/978-3-540-31989-4_27",
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DOI = "doi:10.1007/b107383",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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abstract = "This paper presents an algorithm for induction of
ensembles of decision trees, also referred to as
decision forests. In order to achieve high
expressiveness the trees induced are multivariate, with
various, possibly user-defined tests in their internal
nodes. Strongly typed genetic programming is used to
evolve structure of the tests. Special attention is
given to the problem of diversity of the forest
constructed. An approach is proposed, which explicitly
encourages the induction algorithm to produce a
different tree each run, which represents an
alternative description of the data. It is shown that
forests constructed with this approach have
significantly reduced classification error even for
small forest size, compared to other ensemble methods.
Classification accuracy of the algorithm is also
compared to other recent methods on several real-world
datasets.",
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notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
conjunction with EvoCOP2005 and EvoWorkshops2005",
- }
Genetic Programming entries for
Jan Suchy
Jiri Kubalik
Citations