Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Combining automatic and manual index representations in probabilistic retrieval
Journal of the American Society for Information Science
Method combination for document filtering
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Combining classifiers in text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A meta-learning approach for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Probabilistic combination of text classifiers using reliability indicators: models and results
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Combining Multiple Learning Strategies for Effective Cross Validation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A high performance prototype system for chinese text categorization
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Combining bi-gram of character and word to classify two-class chinese texts in two steps
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
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This paper proposes a two-step method for Chinese text categorization (TC). In the first step, a Naïve Bayesian classifier is used to fix the fuzzy area between two categories, and, in the second step, the classifier with more subtle and powerful features is used to deal with documents in the fuzzy area, which are thought of being unreliable in the first step. The preliminary experiment validated the soundness of this method. Then, the method is extended from two-class TC to multi-class TC. In this two-step framework, we try to further improve the classifier by taking the dependences among features into consideration in the second step, resulting in a Causality Naïve Bayesian Classifier.