Abstract
An understandable classification models is very useful to human experts. Currently, SVM classifiers have good classification performance; however, their classification model is non-understandable. In this paper, we build DRC-BK, a decision rule classifier, which is based on structural risk minimization theory. Experiment results on UCI dataset and Reuters21578 dataset show that DRC-BK has excellent classification performance and excellent scalability, and that when applied with MPDNF kernel, DRC-BK performances the best.
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Zhang, Y., Li, Z., Cui, K. (2005). DRC-BK: Mining Classification Rules by Using Boolean Kernels. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3480. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424758_23
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DOI: https://doi.org/10.1007/11424758_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25860-5
Online ISBN: 978-3-540-32043-2
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