A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{bojarczuk:2004:EMBM,
  author =       "Celia C. Bojarczuk and Heitor S. Lopes and 
                 Alex A. Freitas and Edson L Michalkiewicz",
  title =        "A constrained-syntax genetic programming system for
                 discovering classification rules: application to
                 medical data sets",
  journal =      "Artificial Intelligence in Medicine",
  year =         "2004",
  volume =       "30",
  number =       "1",
  pages =        "27--48",
  month =        jan,
  ISSN =         "0933-3657",
  keywords =     "genetic algorithms, genetic programming, data mining,
                 classification, medical applications",
  URL =          "http://www.cs.kent.ac.uk/people/staff/aaf/my-publications-ukc.html",
  URL =          "http://www.cpgei.cefetpr.br/~hslopes/publicacoes/2004/aim2004.pdf",
  URL =          "http://www.sciencedirect.com/science/article/B6T4K-4B42BDH-1/2/77bc597c3188977bd9ffb40ba10802ac",
  URL =          "http://www.harcourt-international.com/journals/aiim/",
  DOI =          "doi:10.1016/j.artmed.2003.06.001",
  abstract =     "We propose a constrained-syntax genetic programming
                 (GP) algorithm for discovering classification rules in
                 medical data sets. The proposed GP contains several
                 syntactic constraints to be enforced by the system
                 using a disjunctive normal form representation, so that
                 individuals represent valid rule sets that are easy to
                 interpret. The GP is compared with C4.5, a well-known
                 decision-tree-building algorithm, and with another GP
                 that uses Boolean inputs (BGP), in five medical data
                 sets: chest pain, Ljubljana breast cancer, dermatology,
                 Wisconsin breast cancer, and pediatric adrenocortical
                 tumour. For this last data set a new preprocessing step
                 was devised for survival prediction. Computational
                 experiments show that, overall, the GP algorithm
                 obtained good results with respect to predictive
                 accuracy and rule comprehensibility, by comparison with
                 C4.5 and BGP.",
}

Genetic Programming entries for Celia Cristina Bojarczuk Heitor Silverio Lopes Alex Alves Freitas Edson L Michalkiewicz

Citations