RECIPE: A Grammar-based Framework for Automatically Evolving Classification Pipelines
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{deSa:2017:EuroGP,
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author = "Alex G. C. {de Sa} and Walter Jose G. S. Pinto and
Luiz Otavio V. B. Oliveira and Gisele Pappa",
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title = "{RECIPE}: A Grammar-based Framework for Automatically
Evolving Classification Pipelines",
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booktitle = "EuroGP 2017: Proceedings of the 20th European
Conference on Genetic Programming",
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year = "2017",
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month = "19-21 " # apr,
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editor = "Mauro Castelli and James McDermott and
Lukas Sekanina",
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series = "LNCS",
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volume = "10196",
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publisher = "Springer Verlag",
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address = "Amsterdam",
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pages = "246--261",
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organisation = "species",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-319-55695-6",
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code_url = "https://github.com/laic-ufmg/Recipe",
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DOI = "doi:10.1007/978-3-319-55696-3_16",
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abstract = "Automatic Machine Learning is a growing area of
machine learning that has a similar objective to the
area of hyper-heuristics: to automatically recommend
optimized pipelines, algorithms or appropriate
parameters to specific tasks without much dependency on
user knowledge. The background knowledge required to
solve the task at hand is actually embedded into a
search mechanism that builds personalized solutions to
the task. Following this idea, this paper proposes
RECIPE (REsilient ClassifIcation Pipeline Evolution), a
framework based on grammar-based genetic programming
that builds customized classification pipelines. The
framework is flexible enough to receive different
grammars and can be easily extended to other machine
learning tasks. RECIPE overcomes the drawbacks of
previous evolutionary-based frameworks, such as
generating invalid individuals, and organizes a high
number of possible suitable data pre-processing and
classification methods into a grammar. Results of
f-measure obtained by RECIPE are compared to those two
state-of-the-art methods, and shown to be as good as or
better than those previously reported in the
literature. RECIPE represents a first step towards a
complete framework for dealing with different machine
learning tasks with the minimum required human
intervention.",
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notes = "cites \cite{Olson:2016:GECCO}
Also known as desa2017recipe
Part of \cite{Castelli:2017:GP} EuroGP'2017 held
inconjunction with EvoCOP2017, EvoMusArt2017 and
EvoApplications2017",
- }
Genetic Programming entries for
Alex G C de Sa
Walter Jose Goncalves da Silva Pinto
Luiz Otavio Vilas Boas Oliveira
Gisele L Pappa
Citations