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.
CITATION STYLE
Hirsch, L., Saeedi, M., & Hirsch, R. (2004). Evolving Text Classifiers with Genetic Programming. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3003, 309–317. https://doi.org/10.1007/978-3-540-24650-3_29
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