Machine Learning
ECML '93 Proceedings of the European Conference on Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A novel refinement approach for text categorization
Proceedings of the 14th ACM international conference on Information and knowledge management
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Classification based on predictive association rules (CPAR) is a kind of association classification methods which combines the advantages of both associative classification and traditional rule-based classification. For rule generation, CPAR is more efficient than traditional rule-based classification because much repeated calculation is avoided and multiple literals can be selected to generate multiple rules simultaneously. Despite these advantages above in rule generation, the prediction processes have the weaknesses of class rule distribution imbalance and interruption of incorrect class rules. Further, it is useless to instances satisfying no rules. To tackle these problems, this paper presents Class Weighting Adjustment, Center Vector-based Preclassification and Post-processing with Support Vector Machine. Experiments on Chinese text classification corpus TanCorp show that our algorithm achieves an average improvement of 5.91% on F1 score compared with CPAR.