Extending naïve Bayes classifiers using long itemsets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Instance-Based Classification by Emerging Patterns
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Learning of Boolean Functions Using Support Vector Machines
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
On a Capacity Control Using Boolean Kernels for the Learning of Boolean Functions
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms
Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms
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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.