Evolving Rules for Document Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Laurence Hirsch and Masoud Saeedi and Robin Hirsch",
  editor =       "Maarten Keijzer and Andrea Tettamanzi and 
                 Pierre Collet and Jano I. {van Hemert} and Marco Tomassini",
  title =        "Evolving Rules for Document Classification",
  booktitle =    "Proceedings of the 8th European Conference on Genetic
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3447",
  year =         "2005",
  address =      "Lausanne, Switzerland",
  month =        "30 " # mar # " - 1 " # apr,
  organisation = "EvoNet",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-25436-6",
  pages =        "85--95",
  DOI =          "doi:10.1007/b107383",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  abstract =     "We describe a novel method for using Genetic
                 Programming to create compact classification rules
                 based on 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 because
                 the induced rules are meaningful to a human analyst
                 they may have a number of other uses beyond
                 classification and provide a basis for text mining
  notes =        "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
                 conjunction with EvoCOP2005 and EvoWorkshops2005",

Genetic Programming entries for Laurence Hirsch Masoud Saeedi Robin Hirsch