An Evolutionary Computation Approach to Cognitive States Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Rafael Ramirez and Montserrat Puiggros",
  title =        "An Evolutionary Computation Approach to Cognitive
                 States Classification",
  booktitle =    "2007 IEEE Congress on Evolutionary Computation",
  year =         "2007",
  editor =       "Dipti Srinivasan and Lipo Wang",
  pages =        "1793--1799",
  address =      "Singapore",
  month =        "25-28 " # sep,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "1-4244-1340-0",
  file =         "1588.pdf",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2007.4424690",
  abstract =     "The study of human brain functions has dramatically
                 increased in recent years greatly due to the advent of
                 Functional Magnetic Resonance Imaging. This paper
                 presents a genetic programming approach to the problem
                 of classifying the instantaneous cognitive state of a
                 person based on his/her functional Magnetic Resonance
                 Imaging data. The problem provides a very interesting
                 case study of training classifiers with extremely high
                 dimensional, sparse and noisy data. We apply genetic
                 programming for both feature selection and classifier
                 training. We present a successful case study of induced
                 classifiers which accurately discriminate between
                 cognitive states produced by listening to different
                 auditory stimuli.",
  notes =        "CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C",

Genetic Programming entries for Rafael Ramirez Montserrat Puiggros