A simple statistical model and association rule filtering for classification
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Subcellular Localization Prediction through Boosting Association Rules
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Improving the performance of association classifiers by rule prioritization
Knowledge-Based Systems
Two scalable algorithms for associative text classification
Information Processing and Management: an International Journal
ACNB: Associative Classification Mining Based on Naïve Bayesian Method
International Journal of Information Technology and Web Engineering
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Associative classification is a novel and powerful method originating from association rule mining. In the previous studies, a relatively small number of high-quality association rules were used in the prediction. We propose a new approach in which a large number of association rules are generated. Then, the rules are filtered using a new method which is equivalent to a deterministic Boosting algorithm. Through this equivalence, our approach effectively adapts to large-scale classification tasks such as text categorization. Experiments with various text collections show that our method achieves one of the best prediction performance compared with the state-of-the-arts of this field.