Knowledge mining with genetic programming methods for variable selection in flavor design
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Vladislavleva:2010:gecco,
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author = "Katya Vladislavleva and Kalyan Veeramachaneni and
Matt Burland and Jason Parcon and Una-May O'Reilly",
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title = "Knowledge mining with genetic programming methods for
variable selection in flavor design",
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booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
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year = "2010",
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editor = "Juergen Branke and Martin Pelikan and Enrique Alba and
Dirk V. Arnold and Josh Bongard and
Anthony Brabazon and Juergen Branke and Martin V. Butz and
Jeff Clune and Myra Cohen and Kalyanmoy Deb and
Andries P Engelbrecht and Natalio Krasnogor and
Julian F. Miller and Michael O'Neill and Kumara Sastry and
Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and
Carsten Witt",
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isbn13 = "978-1-4503-0072-8",
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pages = "941--948",
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keywords = "genetic algorithms, genetic programming",
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month = "7-11 " # jul,
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organisation = "SIGEVO",
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address = "Portland, Oregon, USA",
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DOI = "doi:10.1145/1830483.1830651",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "This paper presents a novel approach for knowledge
mining from a sparse and repeated measures dataset.
Genetic programming based symbolic regression is
employed to generate multiple models that provide
alternate explanations of the data. This set of models,
called an ensemble, is generated for each of the
repeated measures separately. These multiple ensembles
are then used to generate information about, (a) which
variables are important in each ensemble, (b) cluster
the ensembles into different groups that have similar
variables that drive their response variable, and (c)
measure sensitivity of response with respect to the
important variables. We apply our methodology to a
sensory science dataset. The data contains hedonic
evaluations (liking scores), assigned by a diverse set
of human testers, for a small set of flavors composed
from seven ingredients. Our approach: (1) identifies
the important ingredients that drive the liking score
of a panelist and (2) segments the panelists into
groups that are driven by the same ingredient, and (3)
enables flavour scientists to perform the sensitivity
analysis of liking scores relative to changes in the
levels of important ingredients.",
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notes = "Also known as \cite{1830651} GECCO-2010 A joint
meeting of the nineteenth international conference on
genetic algorithms (ICGA-2010) and the fifteenth annual
genetic programming conference (GP-2010)",
- }
Genetic Programming entries for
Ekaterina (Katya) Vladislavleva
Kalyan Veeramachaneni
Matt Burland
Jason Parcon
Una-May O'Reilly
Citations