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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References
Liu, B., Hsu, W., Ma, Y.: Integrating Classification and Association Rule Mining. In: Proc. the 4th International Conference on Knowledge Discovery and Data Mining (KDD 98), NY (1998)
Li, W., Han, J., Pei, J.: CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules. In: IEEE International Conference on Data Mining (ICDM’01), IEEE Computer Society Press, Los Alamitos (2001)
Yin, X., Han, J.: CPAR: Classification Based on Predictive Association Rules. In: Proc. 2003 SIAM Int’l Conf. Data Mining (SDM ’03) (2003)
Li, J., Dong, G., Ramamohanarao, K., Wong, L.: DeEPs: A New Instance-Based Lazy Discovery and Classification System. Machine Learning 54(2), 99–124 (2004)
Baralis, E., Garza, P.: A lazy approach to pruning classification rules. In: Proc. of the IEEE 2002 International Conference on Data Mining (ICDM’02), Maebashi City, Japan, pp. 35–42. IEEE Computer Society Press, Los Alamitos (2002)
Baralis, E., Garza, P.: Majority Classification by Means of association Rules. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, Springer, Heidelberg (2003)
Sucahyo, Y.G., Gopalan, R.: Building a More Accurate Classifier Based on Strong Frequent Patterns. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, Springer, Heidelberg (2004)
Yang, Z., Zhanhuai, L., Kebin, C.: DRC-BK: Mining Classification Rules by Using Boolean Kernels. In: Gervasi, O., Gavrilova, M., Kumar, V., Laganà, A., Lee, H.P., Mun, Y., Taniar, D., Tan, C.J.K. (eds.) ICCSA 2005. LNCS, vol. 3480, Springer, Heidelberg (2005)
Yang, Z., Zhanhuai, L., Yan, T., Kebin, C.: DRC-BK: Mining Classification Rules with Help of SVM. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, Springer, Heidelberg (2004)
Cristianini, J., Shawe-Taylor,: An Introduction to Support Vector Machines. Cambridge Press, Cambridge (2000)
Zhang, X., Dong, G., Kotagiri, R.: Information-Based Classification by Aggregating Emerging Patterns. Lecture Notes in Computer Science, Hong Kong, pp. 48–53 (2000)
Sadohara, K.: Learning of Boolean functions using support vector machines. In: Abe, N., Khardon, R., Zeugmann, T. (eds.) ALT 2001. LNCS (LNAI), vol. 2225, pp. 106–118. Springer, Heidelberg (2001)
Agrawal, R., Imielinski, T.: Mining association rules between sets of items in large databases. In: Proc. ACM international conference on Management of data (SIGMOD’93), ACM Press, New York (1993)
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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
Print ISBN: 978-3-540-73322-5
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