A wrapper approach with support vector machines for text categorization

Text Categorization (TC)-the assignment of predefined categories to documents of a corpus-plays an important role in a wide variety of information organization and management tasks of Information Retrieval (IR). It involves the management of a lot of information, but some of them could be noisy or irrelevant and hence, a previous feature reduction could improve the performance of the classification. In this paper we proposed a wrapper approach. This kind of approach is time-consuming and sometimes could be infeasible. But our wrapper explores a reduced number of feature subsets and also it uses Support Vector Machines (SVM) as the evaluation system; and this two properties make the wrapper fast enough to deal with large number of features present in text domains. Taking the Reuters-21578 corpus, we also compare this wrapper with the common approach for feature reduction widely applied in TC, which consists of filtering according to scoring measures.