Application of genetic programming for multicategory pattern classification

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{kishore:2000:mpc,
  author =       "J. K. Kishore and L. M. Patnaik and V. Mani and 
                 V. K. Agrawal",
  title =        "Application of genetic programming for multicategory
                 pattern classification",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2000",
  volume =       "4",
  number =       "3",
  pages =        "242--258",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, pattern
                 classification, multicategory pattern classification,
                 GP, distribution-free methods, statistical
                 distribution, two-category classification, discriminant
                 function, association strength measure, SA measure,
                 heuristic rules, training sets, incremental learning,
                 function set choice, conflict resolution",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/4235.873235",
  size =         "17 pages",
  abstract =     "Explores the feasibility of applying genetic
                 programming (GP) to multicategory pattern
                 classification problem. GP can discover relationships
                 and express them mathematically. GP-based techniques
                 have an advantage over statistical methods because they
                 are distribution-free, i.e., no prior knowledge is
                 needed about the statistical distribution of the data.
                 GP also automatically discovers the discriminant
                 features for a class. GP has been applied for
                 two-category classification. A methodology for GP-based
                 n-class classification is developed. The problem is
                 modeled as n two-class problems, and a genetic
                 programming classifier expression (GPCE) is evolved as
                 a discriminant function for each class. The GPCE is
                 trained to recognize samples belonging to its own class
                 and reject others. A strength of association (SA)
                 measure is computed for each GPCE to indicate the
                 degree to which it can recognize samples of its own
                 class. SA is used for uniquely assigning a class to an
                 input feature vector. Heuristic rules are used to
                 prevent a GPCE with a higher SA from swamping one with
                 a lower SA. Experimental results are presented to
                 demonstrate the applicability of GP for multicategory
                 classification, and they are found to be satisfactory.
                 We also discuss the various issues that arise in our
                 approach to GP-based classification, such as the
                 creation of training sets, the role of incremental
                 learning, and the choice of function set in the
                 evolution of GPCE, as well as conflict resolution for
                 uniquely assigning a class.",
  notes =        "comparison in \cite{yu:2004:ECDM} Also known as
                 \cite{873235}",
}

Genetic Programming entries for J K Kishore L M Patnaik V Mani V K Agrawal

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