Evolving text classification rules with genetic programming

HIRSCH, Laurence, SAEEDI, Masoud and HIRSCH, Robin (2005). Evolving text classification rules with genetic programming. Applied Artificial Intelligence: An International Journal, 19 (7), 659-676.

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Official URL: http://www.tandfonline.com/doi/abs/10.1080/0883951...
Link to published version:: 10.1080/08839510590967307

Abstract

We describe a novel method for using genetic programming to create compact classification rules using 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 the rules may have a number of other uses beyond classification and provide a basis for text mining applications.

Item Type: Article
Identification Number: 10.1080/08839510590967307
Depositing User: Laurence Hirsch
Date Deposited: 24 Jan 2013 15:56
Last Modified: 09 Nov 2016 22:54
URI: http://shura.shu.ac.uk/id/eprint/6620

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