Abstract
Considering the interpretability of association classifier, and high classification accuracy of SVM, in this paper, we propose ACIK, an association classifier built with help of SVM, so that the classifier has an interpretable classification model, and has excellent classification accuracy. We also present a novel family of Boolean kernel, namely itemset kernel. ACIK, which takes SVM as learning engine, mines interesting association rules for construct itemset kernels, and then mines the classification weight of these rules from the classification hyperplane constructed by SVM. Experiment results on UCI dataset show that ACIK outperforms some state-of-art classifiers, such as CMAR, CPAR, L3, DeEPs, linear SVM, and so on.
This work is supported by Talent Fund of Northwest A&F University (01140402, 01140401) and Young Cadreman Supporting Program of Northwest A&F University.
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Zhang, Y., Liu, Y., Jing, X., Yan, J. (2007). ACIK : Association Classifier Based on Itemset Kernel. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_86
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DOI: https://doi.org/10.1007/978-3-540-73325-6_86
Publisher Name: Springer, Berlin, Heidelberg
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