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Evolving Text Classifiers with Genetic Programming

Hirsch, L; Saeedi, M; Hirsch, R; (2004) Evolving Text Classifiers with Genetic Programming. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). (pp. 309 - 317).

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We describe a method for using Genetic Programming (GP) to evolve document classifiers. GP's create regular expression type specifications consisting of particular sequences and patterns of N-Grams (character strings) and acquire fitness by producing expressions, which match documents in a particular category but do not match documents in any other category. Libraries of N-Gram patterns have been evolved against sets of pre-categorised training documents and are used to discriminate between new texts. We describe a basic set of functions and terminals and provide results from a categorisation task using the 20 Newsgroup data. © Springer-Verlag 2004.

Type: Proceedings paper
Title: Evolving Text Classifiers with Genetic Programming
UCL classification: UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/141305
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