Genetic Programming of Prototypes for Pattern Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Hugo Jair Escalante and Karlo Mendoza and 
                 Mario Graff and Alicia Morales-Reyes",
  title =        "Genetic Programming of Prototypes for Pattern
  booktitle =    "Proceedings of the 6th Iberian Conference on Pattern
                 Recognition and Image Analysis, {IbPRIA 2013}",
  year =         "2013",
  editor =       "Joao M. Sanches and Luisa Mico and Jaime S. Cardoso",
  volume =       "7887",
  series =       "Lecture Notes in Computer Science",
  pages =        "100--107",
  address =      "Funchal, Madeira, Portugal",
  month =        jun # " 5-7",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  bibdate =      "2013-05-28",
  bibsource =    "DBLP,
  isbn13 =       "978-3-642-38627-5",
  URL =          "",
  DOI =          "doi:10.1007/978-3-642-38628-2_11",
  size =         "8 pages",
  abstract =     "This paper introduces a genetic programming approach
                 to the generation of classification prototypes.
                 Prototype-based classification is a pattern recognition
                 methodology in which the training set of a
                 classification problem is represented by a small subset
                 of instances. The assignment of labels to test
                 instances is usually done by a 1NN rule. We propose a
                 new prototype generation method, based on genetic
                 programming, in which examples of each class are
                 automatically combined to generate highly effective
                 classification prototypes. The genetic program aims to
                 maximise an estimate of the generalisation performance
                 of a 1NN classifier using the prototypes. We report
                 experimental results on a benchmark for the evaluation
                 of prototype generation methods. Experimental results
                 show the validity of our approach: the proposed method
                 outperforms most of the state of the art techniques
                 when using both small and large data sets. Better
                 results are obtained for data sets with numeric
                 attributes only, although the performance of our method
                 on mixed data is very competitive as well.",

Genetic Programming entries for Hugo Jair Escalante Karlo Mario Mendoza Mendoza Mario Graff Guerrero Alicia Morales-Reyes