Abstract
Currently, the accuracy of SVM classifier is very high, but the classification model of SVM classifier is not understandable by human experts. In this paper, we use SVM, which is applied with a Boolean kernel, to construct a hyper-plan for classification, and mine classification rules from this hyper-plane. In this way, we build DRC-BK, a decision rule classifier. Experiment results show that DRC-BK has a higher accuracy than some state-of-art decision rule (decision tree) classifiers, such as C4.5, CBA, CMAR, CAEP and so on.
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References
Yang, Z., Zhanhuai, L., Muling, K., Jianfeng, Y.: Improving the Classification Performance of Boolean Kernels by Applying Occam’s Razor. In: The 2nd International Conference on Computational Intelligence, Robotics and Autonomous Systems, CIRAS 2003 (2003)
Li, W., Han, J., Pei, J.: CMAR: Accurate and Efficient Classification Based on Multiple Class-association Rules. In: Proc. the 2001 IEEE International Conference on Data Mining, ICDM 2001 (2001)
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© 2004 Springer-Verlag Berlin Heidelberg
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Zhang, Y., Li, Z., Tang, Y., Cui, K. (2004). DRC-BK: Mining Classification Rules with Help of SVM. In: Dai, H., Srikant, R., Zhang, C. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science(), vol 3056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_24
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DOI: https://doi.org/10.1007/978-3-540-24775-3_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22064-0
Online ISBN: 978-3-540-24775-3
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