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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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
Departments - Does NOT include content added after October 2018:	Faculty of Science, Technology and Arts > Department of Computing
Identification Number:	https://doi.org/10.1080/08839510590967307
Page Range:	659-676
Depositing User:	Laurence Hirsch
Date Deposited:	24 Jan 2013 15:56
Last Modified:	18 Mar 2021 14:02
URI:	https://shura.shu.ac.uk/id/eprint/6620

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