Associative Classification Using SVM-Based Discretization

Associative classification has been recently proposed which combines association rule mining and classification, and many studies have shown that associative classifiers have high prediction accuracies. In order to apply an asso- ciation rule mining to classification problem, data transfor- mation into the form of transaction data should be preceded before applying association rule mining. In this paper, we propose a discretization method based on Support vector machines, which is very effective for association classifica- tion. The proposed method finds optimal class boundaries by using SVM, and discretization utilizing distances to the boundaries is performed. Experimental results demonstrate that performing SVM-based discretization for continuous attributes makes associative classification more effective in that it reduces the number of association rules mined and also improves the prediction accuracies at the same time.