%0 Journal Article
%T Text categorization based on genetic fuzzy classification and Boosting method<br>基于Boosting算法集成遗传模糊分类器的文本分类
%A LUO Jun
%A KUANG Hang
%A <br>罗军
%A 况夯
%J 计算机应用
%D 2008
%I 
%X A novel method for text categorization, which is based on boosting fuzzy classification, was proposed in the paper. Latent Semantic Index (LSI) was used to select text feature and then Boosting algorithm was proposed to integrate fuzzy classification. In each iteration training of boosting algorithm, the distribution of training instances was adjusted, and classification rules were created by genetic algorithm. The weights of the training instances that were classified correctly by available rules were reduced, so that the new fuzzy rule focuses on the misestimate or uncovered instances. Experimental results show that classifier based on fuzzy classification is effective and efficient.
%K fuzzy classification
%K feature selection
%K Latent Semantic Index (LSI)
%K Boosting algorithm
%K text categorization<br>模糊分类
%K 特征选择
%K 潜在语义索引
%K Boosting算法
%K 文本分类
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=9C019BB9921E6771FD7208FFCF405248&yid=67289AFF6305E306&vid=D3E34374A0D77D7F&iid=9CF7A0430CBB2DFD&sid=BDA0C56EE29C3304&eid=CE8FFD75A1883F19&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=7