An improvement of text association classification using rules weights

Authors:
Xiao-Yun Chen;Yi Chen;Rong-Lu Li;Yun-Fa Hu
Affiliations:
School of Mathematics and Computer Science, Fuzhou University, Fuzhou, China;Department of Computer and Information Technology, Fudan University, Shanghai, China;Department of Computer and Information Technology, Fudan University, Shanghai, China;Department of Computer and Information Technology, Fudan University, Shanghai, China
Venue:
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Year:
2005

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Abstract

Recently, categorization methods based on association rules have been given much attention. In general, association classification has the higher accuracy and the better performance. However, the classification accuracy drops rapidly when the distribution of feature words in training set is uneven. Therefore, text categorization algorithm Weighted Association Rules Categorization (WARC) is proposed in this paper. In this method, association rules are used to classify training samples and rule intensity is defined according to the number of misclassified training samples. Each strong rule is multiplied by factor less than 1 to reduce its weight while each weak rule is multiplied by factor more than 1 to increase its weight. The result of research shows that this method can remarkably improve the accuracy of association classification algorithms by regulation of rules weights.