Feature selection and negative evidence in automated text categorization

We tackle two different problems of text categorization (TC), namely feature selection and classifier induction. We propose a novel FS technique, based on a simplified version of the X 2 statistics and a novel variant, based on the exploitation of negative evidence, of the well-known k-NN method. We report the results of systematic experimentation of these two methods performed on the standard Reuters-21578 benchmark.