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Text Categorization, which consists of automatically assigning documents to a set of categories, usually involves the management of a huge number of features. Most of them are irrelevant and others introduce noise which could mislead the classifiers. Thus, feature reduction is often performed in order to increase the efficiency and effectiveness of the classification. In this paper, we propose to select relevant features by means of a family of linear filtering measures which are simpler than the usual measures applied for this purpose. We carry out experiments over two different corpora and find that the proposed measures perform better than the existing ones.
Index Terms- Text categorization, feature selection, filtering measures, machine learning.

J. Ranilla, E. F. Combarro, R. Mones, E. Monta? and I. D?az, "Introducing a Family of Linear Measures for Feature Selection in Text Categorization," in IEEE Transactions on Knowledge & Data Engineering, vol. 17, no. , pp. 1223-1232, 2005.
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