Evolving rules for document classification

HIRSCH, Laurence, SAEEDI, M and HIRSCH, R (2005). Evolving rules for document classification. In: Genetic programming. Lecture Notes in Computer Science (3447). Berlin, Springer, 85-95.

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Official URL: http://link.springer.com/chapter/10.1007/978-3-540...
Link to published version:: https://doi.org/10.1007/978-3-540-31989-4_8
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    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 applications.

    Item Type: Book Section
    Additional Information: 8th European Conference, EuroGP 2005, Lausanne, Switzerland, March 30 - April 1, 2005. Proceedings
    Research Institute, Centre or Group - Does NOT include content added after October 2018: Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
    Departments - Does NOT include content added after October 2018: Faculty of Science, Technology and Arts > Department of Computing
    Identification Number: https://doi.org/10.1007/978-3-540-31989-4_8
    Page Range: 85-95
    Depositing User: Laurence Hirsch
    Date Deposited: 25 Feb 2013 16:37
    Last Modified: 12 May 2018 07:23
    URI: http://shura.shu.ac.uk/id/eprint/6622

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