Single and multi-objective ant programming for mining interesting rare association rules

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

  author =       "Juan Luis Olmo and Jose Raul Romero and 
                 Sebastian Ventura",
  title =        "Single and multi-objective ant programming for mining
                 interesting rare association rules",
  journal =      "International Journal of Hybrid Intelligent Systems",
  year =         "2014",
  volume =       "11",
  number =       "3",
  pages =        "197--209",
  keywords =     "genetic algorithms, genetic programming, Data mining,
                 rare association rule mining, ant programming",
  ISSN =         "1448-5869",
  DOI =          "doi:10.3233/HIS-140195",
  size =         "13 pages",
  abstract =     "Extracting frequent and reliable rules has been the
                 main interest of the association task of data mining.
                 However, the discovery or infrequent or rare rules is
                 attracting a lot of interest in many domains, such as
                 banking frauds, biomedical data and network intrusion.
                 Most of existent solutions for discovering reliable
                 rules that rarely appear are based on exhaustive
                 classical approaches, which have the drawback of
                 becoming infeasible when dealing with high complex data
                 sets, and which do not take into account any measure of
                 the interestingness of the rules mined. This paper
                 explores the application of ant programming, a
                 bio-inspired technique for finding computer programs,
                 to the discovery of rare association rules. To this
                 end, it proposes two algorithms: a first one which
                 evaluates individuals generated from a single-objective
                 point of view, and a second one which considers
                 simultaneously several objectives to evaluate
                 individuals' fitness. Both of them show their ability
                 to find a high reliable and interesting set of rare
                 rules for the data miner in a short period of time,
                 lacking the drawbacks of exhaustive algorithms.",

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