Evolving rules for document classification

Authors:
Laurence Hirsch;Masoud Saeedi;Robin Hirsch
Affiliations:
School of Management, Royal Holloway University of London, Surrey, UK;School of Management, Royal Holloway University of London, Surrey, UK;University College London, London, UK
Venue:
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Year:
2005

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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.