Publication
Title
Knowledge mining with genetic programming methods for variable selection in flavor design
Author
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 utilized 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 flavor scientists to perform the sensitivity analysis of liking scores relative to changes in the levels of important ingredients.
Language
English
Source (book)
GECCO '10 : proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, Portland, Oregon, USA, 2010 / Branke, Jürgen [edit.]; et al. [edit.]
Publication
S.l. : ACM , 2010
DOI
10.1145/1830483.1830651
Volume/pages
p. 941-948
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
Identifier
Creation 23.02.2011
Last edited 07.10.2022
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