A genetic algorithm for text classification rule induction

Abstract

This paper presents a Genetic Algorithm, called Olex-GA, for the induction of rule-based text classifiers of the form "classify document d under category c if t 1∈ ∈d or ... or t n ∈ ∈d and not (t n∈+∈1∈ ∈d or ... or t n∈+∈m ∈ ∈d) holds", where each t i is a term. Olex-GA relies on an efficient several-rules-per- individual binary representation and uses the F-measure as the fitness function. The proposed approach is tested over the standard test sets Reuters-21578 and Ohsumed and compared against several classification algorithms (namely, Naive Bayes, Ripper, C4.5, SVM). Experimental results demonstrate that it achieves very good performance on both data collections, showing to be competitive with (and indeed outperforming in some cases) the evaluated classifiers. © 2008 Springer-Verlag Berlin Heidelberg.