Title
Learning a lot from only a little : genetic programming for panel segmentation on sparse sensory evaluation data Learning a lot from only a little : genetic programming for panel segmentation on sparse sensory evaluation data
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
conferenceObject
Publication
Berlin :Springer, [*]
Subject
Computer. Automation
Source (book)
Proceedings of the 13th European Conference on Genetic Programming, April 7-9, 2010, Istanbul, Turkey
ISBN - Hoofdstuk
978-3-642-12147-0
ISI
000278827300021
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
We describe a data mining framework that derives panelist information from sparse flavour survey data. One component of the framework executes genetic programming ensemble based symbolic regression. Its evolved models for each panelist provide a second component with all plausible and uncorrelated explanations of how a panelist rates flavours. The second component bootstraps the data using an ensemble selected from the evolved models, forms a probability density function for each panelist and clusters the panelists into segments that are easy to please, neutral, and hard to please.
E-info
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