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.
Hirsch_Evolving_Text_Classification_Rules.pdf - Accepted Version
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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.
|Depositing User:||Laurence Hirsch|
|Date Deposited:||24 Jan 2013 15:56|
|Last Modified:||09 Nov 2016 22:54|
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