RECIPE: A Grammar-based Framework for Automatically Evolving Classification Pipelines

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{deSa:2017:EuroGP,
  author =       "Alex G. C. {de Sa} and Walter Jose G. S. Pinto and 
                 Luiz Otavio V. B. Oliveira and Gisele Pappa",
  title =        "RECIPE: A Grammar-based Framework for Automatically
                 Evolving Classification Pipelines",
  booktitle =    "EuroGP 2017: Proceedings of the 20th European
                 Conference on Genetic Programming",
  year =         "2017",
  month =        "19-21 " # apr,
  editor =       "Mauro Castelli and James McDermott and 
                 Lukas Sekanina",
  series =       "LNCS",
  volume =       "10196",
  publisher =    "Springer Verlag",
  address =      "Amsterdam",
  pages =        "246--261",
  organisation = "species",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1007/978-3-319-55696-3_16",
  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.",
  notes =        "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