Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Applying Cascaded Feature Selection to SVM Text Categorization
DEXA '02 Proceedings of the 13th International Workshop on Database and Expert Systems Applications
Feature selection for text categorization on imbalanced data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Semantic Feature Selection Using WordNet
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
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Feature selection is one of the key technologies for text categorization. Currently, it mainly includes technologies based statistics which is primarily from information theory and technologies based semantics which covers natural language processing, semantic web etc., Based on Poisson Hypothesis, this article presents a new method combining both and tries to find features in documents with more semantic information. The contrast experiments carried on the Reuters-21578 corpus with the IG, Chi2 and WN algorithms show that this method has more advantages than other algorithms.