Knowledge mining sensory evaluation data: genetic programming, statistical techniques, and swarm optimization
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10710_2011_Article_9153.pdf
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Author(s)
Vladislavleva, Ekaterina
O'Reilly, Una-May
Veeramachaneni, Kalyan
Date Issued
January 2012
Journal
Genetic Programming and Evolvable Machines
Publisher
Springer US
Citation
Veeramachaneni, Kalyan, Ekaterina Vladislavleva, and Una-May O’Reilly. “Knowledge Mining Sensory Evaluation Data: Genetic Programming, Statistical Techniques, and Swarm Optimization.” Genetic Programming and Evolvable Machines 13.1 (2012): 103–133.
Version
Author's final manuscript
Abstract
Knowledge mining sensory evaluation data is a challenging process due to extreme sparsity of the data, and a large variation in responses from different members (called assessors) of the panel. The main goals of knowledge mining in sensory sciences are understanding the dependency of the perceived liking score on the concentration levels of flavors’ ingredients, identifying ingredients that drive liking, segmenting the panel into groups with similar liking preferences and optimizing flavors to maximize liking per group. Our approach employs (1) Genetic programming (symbolic regression) and ensemble methods to generate multiple diverse explanations of assessor liking preferences with confidence information; (2) statistical techniques to extrapolate using the produced ensembles to unobserved regions of the flavor space, and segment the assessors into groups which either have the same propensity to like flavors, or are driven by the same ingredients; and (3) two-objective swarm optimization to identify flavors which are well and consistently liked by a selected segment of assessors.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s10710-011-9153-2
