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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Vladislavleva:2010:gecco,

• 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

Citations