On the use of multi-objective evolutionary algorithms for the induction of fuzzy classification rule systems

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@Article{Setzkorn:2005:BioSystems,
  author =       "Christian Setzkorn and Ray C. Paton",
  title =        "On the use of multi-objective evolutionary algorithms
                 for the induction of fuzzy classification rule
                 systems",
  journal =      "BioSystems",
  year =         "2005",
  volume =       "81",
  number =       "2",
  pages =        "101--112",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, Supervised
                 classification, Multi-objective evolutionary
                 algorithms, Fuzzy classification rule systems",
  DOI =          "doi:10.1016/j.biosystems.2005.02.003",
  abstract =     "Extracting comprehensible and general classifiers from
                 data in the form of rule systems is an important task
                 in many problem domains. This study investigates the
                 utility of a multi-objective evolutionary algorithm
                 (MOEA) for this task. Multi-objective evolutionary
                 algorithms are capable of finding several trade-off
                 solutions between different objectives in a single run.
                 In the context of the present study, the objectives to
                 be optimised are the complexity of the rule systems,
                 and their fit to the data. Complex rule systems are
                 required to fit the data well. However, overly complex
                 rule systems often generalise poorly on new data. In
                 addition they tend to be incomprehensible. It is,
                 therefore, important to obtain trade-off solutions that
                 achieve the best possible fit to the data with the
                 lowest possible complexity. The rule systems produced
                 by the proposed multi-objective evolutionary algorithm
                 are compared with those produced by several other
                 existing approaches for a number of benchmark datasets.
                 It is shown that the algorithm produces less complex
                 classifiers that perform well on unseen data.",
}

Genetic Programming entries for Christian Setzkorn Ray C Paton

Citations