Abstract
Many studies in data mining have proposed a new classification approach called associative classification. According to several reports associative classification achieves higher classification accuracy than do traditional classification approaches. However, the associative classification suffers from a major drawback: it is based on the use of a very large number of classification rules; and consequently takes efforts to select the best ones in order to construct the classifier. To overcome such drawback, we propose a new associative classification method called Garc that exploits a generic basis of association rules in order to reduce the number of association rules without jeopardizing the classification accuracy. Moreover, Garc proposes a new selection criterion called score, allowing to ameliorate the selection of the best rules during classification. Carried out experiments on 12 benchmark data sets indicate that Garc is highly competitive in terms of accuracy in comparison with popular associative classification methods.