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31. Evolving Rules for Document Classification.

Author

Keijzer, Maarten, Tettamanzi, Andrea, Collet, Pierre, Hemert, Jano van, Tomassini, Marco, Hirsch, Laurence, Saeedi, Masoud, and Hirsch, Robin

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. [ABSTRACT FROM AUTHOR]

Published
2005

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