Evolutionary decision tree induction with multi-interval discretization

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@InProceedings{Saremi:2014:ICIS,
  author =       "Mehrin Saremi and Farzin Yaghmaee",
  title =        "Evolutionary decision tree induction with
                 multi-interval discretization",
  booktitle =    "Iranian Conference on Intelligent Systems (ICIS
                 2014)",
  year =         "2014",
  month =        "4-6 " # feb,
  address =      "Bam",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, decision tree
                 induction, evolutionary algorithm, multi-interval
                 discrimination",
  DOI =          "doi:10.1109/IranianCIS.2014.6802543",
  abstract =     "Decision trees are one of the widely used machine
                 learning tools with their most important advantage
                 being their comprehensible structure. Many classic
                 algorithms (usually greedy top-down ones) have been
                 developed for constructing decision trees, while in
                 recent years evolutionary algorithms have found their
                 application in this area. Discrimination is a technique
                 which enables algorithms like decision trees to deal
                 with continuous attributes as well as discrete
                 attributes. We present an algorithm that combines the
                 process of multi-interval discretisation with tree
                 induction, and introduce especially designed genetic
                 programming operators for this task. We compared our
                 algorithm with a classic one, namely C4.5. The
                 comparison results suggest that our method is capable
                 of producing smaller trees.",
  notes =        "Also known as \cite{6802543}",
}

Genetic Programming entries for Mehrin Saremi Farzin Yaghmaee

Citations