A comparison of GE optimized neural networks and decision trees

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@InProceedings{Hoover:2012:GECCOcomp,
  author =       "Kristopher Hoover and Rachel Marceau and 
                 Tyndall Harris and David Reif and Alison Motsinger-Reif",
  title =        "A comparison of GE optimized neural networks and
                 decision trees",
  booktitle =    "GECCO 2012 Graduate Students Workshop",
  year =         "2012",
  editor =       "Alison Motsinger-Reif",
  isbn13 =       "978-1-4503-1178-6",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  pages =        "611--614",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330784.2330885",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Grammatical evolution neural networks (GENN) is a
                 commonly used method at identifying difficult to detect
                 gene-gene and gene-environment interactions. It has
                 been shown to be an effective tool in the prediction of
                 common diseases using single nucleotide polymorphisms
                 (SNPs). However, GENN lacks interpretability because it
                 is a black box model. Therefore, grammatical evolution
                 of decision trees (GEDT) is being considered as an
                 alternative, as decision trees are easily interpretable
                 for clinicians. Previously, the most effective
                 parameters for GEDT and GENN were found using parameter
                 sweeps. Since GEDT is much more intuitive and easy to
                 understand, it becomes important to compare its
                 predictive power to that of GENN. We show that it is
                 not as effective as GENN at detecting disease causing
                 polymorphisms especially in more difficult to detect
                 models, but this power trade off may be worth it for
                 interpretability.",
  notes =        "Also known as \cite{2330885} Distributed at
                 GECCO-2012.

                 ACM Order Number 910122.",
}

Genetic Programming entries for Kristopher Hoover Rachel Marceau Tyndall Harris David M Reif Alison A Motsinger

Citations