Knowledge mining with genetic programming methods for variable selection in flavor design

Created by W.Langdon from gp-bibliography.bib Revision:1.4394

  author =       "Katya Vladislavleva and Kalyan Veeramachaneni and 
                 Matt Burland and Jason Parcon and Una-May O'Reilly",
  title =        "Knowledge mining with genetic programming methods for
                 variable selection in flavor design",
  booktitle =    "GECCO '10: Proceedings of the 12th annual conference
                 on Genetic and evolutionary computation",
  year =         "2010",
  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",
  isbn13 =       "978-1-4503-0072-8",
  pages =        "941--948",
  keywords =     "genetic algorithms, genetic programming",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830483.1830651",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  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.",
  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