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SEMCO
2008
IEEE

Text Categorization Based on Boosting Association Rules

10 years 3 months ago
Text Categorization Based on Boosting Association Rules
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
Added 01 Jun 2010
Updated 01 Jun 2010
Type Conference
Year 2008
Where SEMCO
Authors Yongwook Yoon, Gary Geunbae Lee
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