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

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@Article{Veeramachaneni:2012:GPEM,
  author =       "Kalyan Veeramachaneni and Ekaterina Vladislavleva and 
                 Una-May O'Reilly",
  title =        "Knowledge mining sensory evaluation data: genetic
                 programming, statistical techniques, and swarm
                 optimization",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2012",
  volume =       "13",
  number =       "1",
  pages =        "103--133",
  month =        mar,
  note =         "Special Section on Evolutionary Algorithms for Data
                 Mining",
  keywords =     "genetic algorithms, genetic programming, food, scent,
                 flavor, Symbolic regression, Sensory science,
                 Ensembles, Non-linear optimisation, Variable selection,
                 Pareto, Hedonic evaluation, Complexity control, PSO,
                 bloat",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-011-9153-2",
  size =         "31 pages",
  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 flavours'
                 ingredients, identifying ingredients that drive liking,
                 segmenting the panel into groups with similar liking
                 preferences and optimising flavors to maximise 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.",
  affiliation =  "CSAIL, MIT, 32 Vassar Street, D-540, Cambridge, MA,
                 USA",
}

Genetic Programming entries for Kalyan Veeramachaneni Ekaterina (Katya) Vladislavleva Una-May O'Reilly

Citations