Ant Programming for Classification Rule Mining. Applications

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@PhdThesis{Olmo-Ortiz:thesis,
  author =       "Juan Luis {Olmo Ortiz}",
  title =        "Ant Programming for Classification Rule Mining.
                 Applications",
  school =       "Departamento de Informtica y Anlisis Numrico,
                 University of Cordoba",
  year =         "2013",
  type =         "Doctor en Informatica",
  address =      "Spain",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.uco.es/grupos/kdis/index.php?option=com_jresearch&view=thesis&task=show&id=5&Itemid=51&lang=en",
  URL =          "http://www.jlolmo.com/docs/Thesis%20Juan%20Luis%20Olmo.pdf",
  size =         "295 pages",
  abstract =     "Many algorithms and techniques have been employed to
                 address the classification task. Recently, Ant Colony
                 Optimisation (ACO) metaheuristic has tackled this task
                 successfully. ACO is a nature inspired optimization
                 metaheuristic which mimic the behaviour and
                 self-organisation of ant colonies in their search for
                 food. On the other hand, Genetic Programming (GP), a
                 particular type of automatic programming where genetic
                 algorithms are used as search technique, also has
                 demonstrated to obtain good results for classification.
                 In contrast, another type of automatic programming
                 known as Ant Programming (AP), which uses ACO instead
                 of genetic algorithms as search technique, has never
                 applied to classification. Considering the good results
                 obtained by both ACO and GP for classification, we
                 consider that it would be interesting to explore the
                 application of AP to this task.

                 The main objective of this thesis can be broke down in
                 the following subobjectives: Carry out a theoretical
                 study of the existent ACO-based algorithms for
                 classification rule mining. Perform a bibliographic
                 review of the several proposals of AP presented to
                 date. Develop an AP model based on the use of a
                 context-free grammar for the extraction of
                 classification rules. Address the classification task
                 from a multi-objective perspective. Adapt the previous
                 model to this approach. Evaluate the implemented models
                 over different problems of actual interest by using
                 standard UCI data: Imbalanced data

                 Intrusion detection systems

                 Text categorisation",
  notes =        "In Spanish.

                 Supervisors Sebastian Ventura and Jose Raul Romero",
}

Genetic Programming entries for Juan Luis Olmo

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