Angular measures for feature selection in text categorization

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 or introduce noise which misleads 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 what we call Angular Measures, which are simpler than other usual measures applied for this purpose. We carry out experiments over two different corpora and find that the proposed measures perform equal or better than some of the existing ones.