Classifying news stories using memory based reasoning
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
A vector space model for automatic indexing
Readings in information retrieval
Using a generalized instance set for automatic text categorization
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Chinese Documents Classification Based on N-Grams
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Text classification: a recent overview
ICCOMP'05 Proceedings of the 9th WSEAS International Conference on Computers
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With the rapid growth of online text information, efficient text classification has become one of the key techniques for organizing and processing text repositories. In this paper, an efficient text classification approach was proposed based on pruning training-corpus. By using the proposed approach, noisy and superfluous documents in training corpuses can be cut off drastically, which leads to substantial classification efficiency improvement. Effective algorithm for training corpus pruning is proposed. Experiments over the commonly used Reuters benchmark are carried out, which validates the effectiveness and efficiency of the proposed approach.