Grammatical evolution support vector machines for predicting human genetic disease association

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@InProceedings{Marvel:2012:GECCOcomp,
  author =       "Skylar Marvel and Alison Motsinger-Reif",
  title =        "Grammatical evolution support vector machines for
                 predicting human genetic disease association",
  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, SVN",
  pages =        "595--598",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330784.2330881",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Identifying genes that predict common, complex human
                 diseases is a major goal of human genetics. This is
                 made difficult by the effect of epistatic interactions
                 and the need to analyze datasets with high-dimensional
                 feature spaces. Many classification methods have been
                 applied to this problem, one of the more recent being
                 Support Vector Machines (SVM). Selection of which
                 features to include in the SVM model and what
                 parameters or kernels to use can often be a difficult
                 task. This work uses Grammatical Evolution (GE) as a
                 way to choose features and parameters. Initial results
                 look promising and encourage further development and
                 testing of this new approach.",
  notes =        "Also known as \cite{2330881} Distributed at
                 GECCO-2012.

                 ACM Order Number 910122.",
}

Genetic Programming entries for Skylar Marvel Alison A Motsinger

Citations