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 hyperplane. 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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CITATION STYLE
Zhang, Y., Li, Z., Tang, Y., & Cui, K. (2004). DRC-BK: Mining classification rules with help of SVM. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3056, pp. 191–195). Springer Verlag. https://doi.org/10.1007/978-3-540-24775-3_24
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