Evolving Text Classifiers with Genetic Programming.
Presented at: UNSPECIFIED.
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:||Conference item (UNSPECIFIED)|
|Title:||Evolving Text Classifiers with Genetic Programming|
|Keywords:||Genetic Programming, N-Gram, Text categorisation|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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