Hirsch, L; Saeedi, M; Hirsch, R; (2005) Evolving rules for document classification. Lecture Notes in Computer Science , 3447 85 - 95.
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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. © Springer-Verlag Berlin Heidelberg 2005.
|Title:||Evolving rules for document classification|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
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