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.
['Yongwook Yoon', 'Gary Geunbae Lee']
Text Categorization Based on Boosting Association Rules