C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Growing decision trees on support-less association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Discovery of Surprising Exception Rules Based on Intensity of Implication
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Improving the Discovery of Association Rules with Intensity of Implication
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An association-based case reduction technique for case-based reasoning
Information Sciences: an International Journal
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In recent years, extensive research has been carried out by using association rules to build more accurate classifiers. The idea behind these integrated approaches is to focus on a limited subset of association rules. This paper aims to contribute to this integrated framework by adapting the Classification Based on Associations (CBA) algorithm. CBA was adapted by coupling it with another measurement of the quality of association rules: i.e. intensity of implication. The new algorithm has been implemented and empirically tested on an authentic financial dataset for purposes of bankruptcy prediction. We validated our results with an association ruleset, with C4.5, with original CBA and with CART by statistically comparing its performance via the area under the ROC-curve. The adapted CBA algorithm presented in this paper proved to generate significantly better results than the other classifiers at the 5% level of significance.