Boosting improves stability and accuracy of genetic programming in biological classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InCollection{Saetrom:2006:GPTP,
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author = "Pal Saetrom and Olaf Rene Birkeland and
Ola {Snove Jr.}",
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title = "Boosting improves stability and accuracy of genetic
programming in biological classification",
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booktitle = "Genetic Programming Theory and Practice {IV}",
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year = "2006",
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editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
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volume = "5",
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series = "Genetic and Evolutionary Computation",
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pages = "61--78",
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address = "Ann Arbor",
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month = "11-13 " # may,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming,
Bioinformatics, microRNA, gene prediction, RNAi",
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ISBN = "0-387-33375-4",
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DOI = "doi:10.1007/978-0-387-49650-4_5",
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size = "16 pages",
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abstract = "Biological sequence analysis presents interesting
challenges for machine learning. Using one of the most
important current problems -- the recognition of
functional target sites for microRNA molecules -- as an
example, we show how joining multiple genetic
programming classifiers improves accuracy and stability
tremendously. When moving from single classifiers to
bagging and boosting with cross validation and
parameter optimisation, you require more computing
power. We use a special-purpose search processor for
fitness evaluation, which renders boosted genetic
programming practical for our purposes.",
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notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007
after the workshop",
- }
Genetic Programming entries for
Pal Saetrom
Olaf Rene Birkeland
Ola Snove Jr
Citations