Autonomous Document Classification for Business

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@TechReport{clack:1996:adcb,
  author =       "Chris Clack and Jonny Farringdon and Peter Lidwell and 
                 Tina Yu",
  title =        "Autonomous Document Classification for Business",
  institution =  "University College London",
  year =         "1996",
  type =         "Research Note",
  number =       "RN/96/48",
  address =      "Computer Science, Gower Street, London, WC1E 6BT, UK",
  month =        jun,
  note =         "Appears in Autonomous Agents '97",
  keywords =     "genetic algorithms, genetic programming, Softbot,
                 agent architecture, pattern recognition, long term
                 adaptation and learning",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/clack_1997_adcb.pdf",
  abstract =     "With the continuing exponential growth of the Internet
                 and the more recent growth of business Intranets, the
                 commercial world is becoming increasingly aware of the
                 problem of electronic information overload. This has
                 encouraged interest in developing agents/softbots that
                 can act as electronic personal assistants and can
                 develop and adapt representations of users information
                 needs, commonly known as profiles.

                 As the result of collaborative research with Friends of
                 the Earth, an environmental issues campaigning
                 organisation, we have developed a general purpose
                 information classification agent architecture and have
                 applied it to the problem of document classification
                 and routing. Collaboration with Friends of the Earth
                 allows us to test our ideas in a non-academic context
                 involving high volumes of documents.

                 We use the technique of genetic programming (GP), (Koza
                 and Rice 1992), to evolve classifying agents. This is a
                 novel approach for document classification, where each
                 agent evolves a parse-tree representation of a user's
                 particular information need. The other unusual feature
                 of our research is the longevity of our agents and the
                 fact that they undergo a continual training process;
                 feedback from the user enables the agent to adapt to
                 the user's long-term information requirements.",
  notes =        "see also \cite{clack:1997:adcb}",
  size =         "8 pages",
}

Genetic Programming entries for Christopher D Clack Jonny Farringdon Peter R Lidwell Tina Yu

Citations