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Knowledge mining sensory evaluation data: genetic programming, statistical techniques, and swarm optimization

Authors
  • Veeramachaneni, Kalyan1
  • Vladislavleva, Ekaterina2
  • O’Reilly, Una-May3
  • 1 CSAIL, MIT, 32 Vassar Street, D-540, Cambridge, MA, USA , Cambridge (United States)
  • 2 Evolved Analytics Europe BVBA, Wijnegem, Belgium , Wijnegem (Belgium)
  • 3 CSAIL, MIT, 32 Vassar Street, D-534, Cambridge, MA, USA , Cambridge (United States)
Type
Published Article
Journal
Genetic Programming and Evolvable Machines
Publisher
Springer US
Publication Date
Jan 20, 2012
Volume
13
Issue
1
Pages
103–133
Identifiers
DOI: 10.1007/s10710-011-9153-2
Source
Springer Nature
Keywords
License
Yellow

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.

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