Evolutionary model trees for handling continuous classes in machine learning

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@Article{Barros2011954,
  author =       "Rodrigo C. Barros and Duncan D. Ruiz and 
                 Marcio P. Basgalupp",
  title =        "Evolutionary model trees for handling continuous
                 classes in machine learning",
  journal =      "Information Sciences",
  year =         "2011",
  volume =       "181",
  number =       "5",
  pages =        "954--971",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 algorithms, Model trees, Continuous classes, Machine
                 learning",
  ISSN =         "0020-0255",
  URL =          "http://www.sciencedirect.com/science/article/B6V0C-51GHWYC-1/2/2ba74d92cb03abc637a4c377b47a4dbe",
  DOI =          "doi:10.1016/j.ins.2010.11.010",
  size =         "18 pages",
  abstract =     "Model trees are a particular case of decision trees
                 employed to solve regression problems. They have the
                 advantage of presenting an interpretable output,
                 helping the end-user to get more confidence in the
                 prediction and providing the basis for the end-user to
                 have new insight about the data, confirming or
                 rejecting hypotheses previously formed. Moreover, model
                 trees present an acceptable level of predictive
                 performance in comparison to most techniques used for
                 solving regression problems. Since generating the
                 optimal model tree is an NP-Complete problem,
                 traditional model tree induction algorithms make use of
                 a greedy top-down divide-and-conquer strategy, which
                 may not converge to the global optimal solution. we
                 propose a novel algorithm based on the use of the
                 evolutionary algorithms paradigm as an alternate
                 heuristic to generate model trees in order to improve
                 the convergence to globally near-optimal solutions. We
                 call our new approach evolutionary model tree induction
                 (E-Motion). We test its predictive performance using
                 public UCI data sets, and we compare the results to
                 traditional greedy regression/model trees induction
                 algorithms, as well as to other evolutionary
                 approaches. Results show that our method presents a
                 good trade-off between predictive performance and model
                 comprehensibility, which may be crucial in many machine
                 learning applications.",
}

Genetic Programming entries for Rodrigo C Barros Duncan Dubugras Ruiz Marcio Porto Basgalupp

Citations