An iterative genetic programming approach to prototype generation

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Jose Maria Valencia-Ramirez and Mario Graff and 
                 Hugo Jair Escalante and Jaime Cerda-Jacobo",
  title =        "An iterative genetic programming approach to prototype
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2017",
  volume =       "18",
  number =       "2",
  pages =        "123--147",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, K-nearest
                 neighbours, Prototype generation, Pattern
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-016-9279-3",
  size =         "25 pages",
  abstract =     "In this paper, we propose a genetic programming (GP)
                 approach to the problem of prototype generation for
                 nearest-neighbour (NN) based classification. The
                 problem consists of learning a set of artificial
                 instances that effectively represents the training set
                 of a classification problem, with the goal of reducing
                 the storage requirements and the computational cost
                 inherent in KNN classifiers. This work introduces an
                 iterative GP technique to learn such artificial
                 instances based on a non-linear combination of
                 instances available in the training set. Experiments
                 are reported in a benchmark for prototype generation.
                 Experimental results show our approach is very
                 competitive with the state of the art, in terms of
                 accuracy and in its ability to reduce the training set

Genetic Programming entries for Jose Maria Valencia-Ramirez Mario Graff Guerrero Hugo Jair Escalante Jamie Cerda