Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Supervised term weighting for automated text categorization
Proceedings of the 2003 ACM symposium on Applied computing
Beyond TFIDF weighting for text categorization in the vector space model
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Integrating background knowledge into RBF networks for text classification
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Boosting for text classification with semantic features
WebKDD'04 Proceedings of the 6th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
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For the computation of feature weights, this paper proposed a novel approach through introducing the concept of feature significance degree on the real rough set theory. The feature significance degree could characterize the contribution of features to the decision making more objectively. The proposed approach was applied to the benchmark test sets Reuters-21578 Top10 and 20 Newsgroups to examine its effectiveness. The results show that the proposed approach improves the distribution status of sample space. It makes the samples in the same class more compact and those in different classes looser.