Effective classification by integrating support vector machine and association rule mining

Authors:
Keivan Kianmehr;Reda Alhajj
Affiliations:
Dept. of Computer Science, University of Calgary, Calgary, Alberta, Canada;Dept. of Computer Science, University of Calgary, Calgary, Alberta, Canada
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

Citing 8
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Abstract

In this study, we propose a new classification framework, CARSVM model, which integrates association rule mining and support vector machine. The aim is to take advantages of both knowledge represented by class association rules and the power of SVM algorithm to construct an efficient and accurate classifier model. Instead of using the original training set, a set of rule-based feature vectors, which are generated based on the discriminative ability of class association rules over the training samples, are presented to the learning process of the SVM algorithm. The reported test results demonstrate the applicability, efficiency and effectiveness of the proposed model.