Evolving Rules for Document Classification

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.

• Type: Conference
• Year: 2005
• Where: EUROGP
• Authors: Laurence Hirsch, Masoud Saeedi, Robin Hirsch
• Keywords: Compact Classification Rules, EUROGP 2005, Programs Acquire Fitness, Reuters 21578 Dataset