Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Learning of Boolean Functions Using Support Vector Machines
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
A Lazy Approach to Pruning Classification Rules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
DeEPs: A New Instance-Based Lazy Discovery and Classification System
Machine Learning
Building a more accurate classifier based on strong frequent patterns
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
| Hi-index | 0.01 |
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.