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.
A genetic algorithm for text classification rule induction
RULLO, Pasquale;
2008-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
