Evolving Text Classification Rules with Genetic Programming

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  title =        "Evolving Text Classification Rules with Genetic
  author =       "Laurence Hirsch and Masoud Saeedi and Robin Hirsch",
  journal =      "Applied Artificial Intelligence",
  year =         "2005",
  number =       "7",
  volume =       "19",
  pages =        "659--676",
  month =        aug,
  bibdate =      "2005-12-01",
  bibsource =    "DBLP,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.journalsonline.tandf.co.uk/openurl.asp?genre=article&issn=0883-9514&volume=19&issue=7&spage=659",
  DOI =          "doi:10.1080/08839510590967307",
  abstract =     "We describe a novel method for using genetic
                 programming to create compact classification rules
                 using combinations of N-grams (character strings).
                 Genetic programs acquire fitness by producing rules
                 that are effective classifiers in terms of precision
                 and recall when evaluated against a set of training
                 documents. We describe a set of functions and terminals
                 and provide results from a classification task using
                 the Reuters 21578 dataset. We also suggest that the
                 rules may have a number of other uses beyond
                 classification and provide a basis for text mining

Genetic Programming entries for Laurence Hirsch Masoud Saeedi Robin Hirsch