Boosting improves stability and accuracy of genetic programming in biological classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Pal Saetrom and Olaf Rene Birkeland and 
                 Ola {Snove Jr.}",
  title =        "Boosting improves stability and accuracy of genetic
                 programming in biological classification",
  booktitle =    "Genetic Programming Theory and Practice {IV}",
  year =         "2006",
  editor =       "Rick L. Riolo and Terence Soule and Bill Worzel",
  volume =       "5",
  series =       "Genetic and Evolutionary Computation",
  pages =        "61--78",
  address =      "Ann Arbor",
  month =        "11-13 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming,
                 Bioinformatics, microRNA, gene prediction, RNAi",
  ISBN =         "0-387-33375-4",
  DOI =          "doi:10.1007/978-0-387-49650-4_5",
  size =         "16 pages",
  abstract =     "Biological sequence analysis presents interesting
                 challenges for machine learning. Using one of the most
                 important current problems -- the recognition of
                 functional target sites for microRNA molecules -- as an
                 example, we show how joining multiple genetic
                 programming classifiers improves accuracy and stability
                 tremendously. When moving from single classifiers to
                 bagging and boosting with cross validation and
                 parameter optimisation, you require more computing
                 power. We use a special-purpose search processor for
                 fitness evaluation, which renders boosted genetic
                 programming practical for our purposes.",
  notes =        "part of \cite{Riolo:2006:GPTP} Published Jan 2007
                 after the workshop",

Genetic Programming entries for Pal Saetrom Olaf Rene Birkeland Ola Snove Jr