Classification rule mining using ant programming guided by grammar with multiple Pareto fronts

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "J. L. Olmo and J. R. Romero and S. Ventura",
  title =        "Classification rule mining using ant programming
                 guided by grammar with multiple {Pareto} fronts",
  journal =      "Soft Computing",
  year =         "2012",
  volume =       "16",
  number =       "12",
  pages =        "2143--2163",
  month =        dec,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Ant
                 programming (AP), Grammar-based automatic programming,
                 Multi-objective ant colony optimisation (MOACO),
                 Classification; Data mining (DM)",
  ISSN =         "1432-7643",
  DOI =          "doi:10.1007/s00500-012-0883-8",
  language =     "English",
  size =         "21 pages",
  abstract =     "This paper proposes a multi-objective ant programming
                 algorithm for mining classification rules, MOGBAP,
                 which focuses on optimizing sensitivity, specificity,
                 and comprehensibility. It defines a context-free
                 grammar that restricts the search space and ensures the
                 creation of valid individuals, and its heuristic
                 function presents two complementary components.
                 Moreover, the algorithm addresses the classification
                 problem from a new multi-objective perspective
                 specifically suited for this task, which finds an
                 independent Pareto front of individuals per class, so
                 that it avoids the overlapping problem that appears
                 when measuring the fitness of individuals from
                 different classes. A comparative analysis of MOGBAP
                 using two and three objectives is performed, and then
                 its performance is experimentally evaluated throughout
                 15 varied benchmark data sets and compared to those
                 obtained using another eight relevant rule extraction
                 algorithms. The results prove that MOGBAP outperforms
                 the other algorithms in predictive accuracy, also
                 achieving a good trade-off between accuracy and

Genetic Programming entries for Juan Luis Olmo Jose Raul Romero Salguero Sebastian Ventura