Discovering New Rule Induction Algorithms with Grammar-based Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InCollection{series/springer/PappaF08,
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title = "Discovering New Rule Induction Algorithms with
Grammar-based Genetic Programming",
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author = "Gisele L. Pappa and Alex Alves Freitas",
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bibdate = "2008-12-16",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/series/springer/MaimonRokach2008.html#PappaF08",
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booktitle = "Soft Computing for Knowledge Discovery and Data
Mining",
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publisher = "Springer",
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year = "2008",
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editor = "Oded Maimon and Lior Rokach",
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isbn13 = "978-0-387-69934-9",
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pages = "133--152",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1007/978-0-387-69935-6_6",
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abstract = "Rule induction is a data mining technique used to
extract classification rules of the form IF
(conditions) THEN (predicted class) from data. The
majority of the rule induction algorithms found in the
literature follow the sequential covering strategy,
which essentially induces one rule at a time until
(almost) all the training data is covered by the
induced rule set. This strategy describes a basic
algorithm composed by several key elements, which can
be modified and/or extended to generate new and better
rule induction algorithms. With this in mind, this work
proposes the use of a grammar-based genetic programming
(GGP) algorithm to automatically discover new
sequential covering algorithms. The proposed system is
evaluated using 20 data sets, and the
automatically-discovered rule induction algorithms are
compared with four well-known human-designed rule
induction algorithms. Results showed that the GGP
system is a promising approach to effectively discover
new sequential covering algorithms.",
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notes = "26 rules in grammar: table 1",
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size = "20 pages",
- }
Genetic Programming entries for
Gisele L Pappa
Alex Alves Freitas
Citations