Evolution of Multiple Tree Structured Patterns from Tree-Structured Data Using Clustering
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{DBLP:conf/ausai/NagamineMKUT08,
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author = "Masatoshi Nagamine and Tetsuhiro Miyahara and
Tetsuji Kuboyama and Hiroaki Ueda and Kenichi Takahashi",
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title = "Evolution of Multiple Tree Structured Patterns from
Tree-Structured Data Using Clustering",
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editor = "Wayne Wobcke and Mengjie Zhang",
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booktitle = "AI 2008: 21st Australasian Joint Conference on
Artificial Intelligence",
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publisher = "Springer",
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series = "Lecture Notes in Computer Science",
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volume = "5360",
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year = "2008",
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pages = "500--511",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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address = "Auckland, New Zealand",
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month = dec # " 1-5",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-540-89377-6",
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DOI = "doi:10.1007/978-3-540-89378-3_51",
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abstract = "We propose a new genetic programming approach to
extraction of multiple tree structured patterns from
tree-structured data using clustering. As a combined
pattern we use a set of tree structured patterns,
called tag tree patterns. A structured variable in a
tag tree pattern can be substituted by an arbitrary
tree. A set of tag tree patterns matches a tree, if at
least one of the set of patterns matches the tree. By
clustering positive data and running GP subprocesses on
each cluster with negative data, we make a combined
pattern which consists of best individuals in GP
subprocesses. The experiments on some glycan data show
that our proposed method has a higher support of about
0.8 while the previous method for evolving single
patterns has a lower support of about 0.5.",
- }
Genetic Programming entries for
Masatoshi Nagamine
Tetsuhiro Miyahara
Tetsuji Kuboyama
Hiroaki Ueda
Ken-ichi Takahashi
Citations