document: Integrating feature and instance selection for text classification Instance selection and feature selection are two orthogonal methods for reducing the amount and complexity of data . Feature selection aims at the reduction of redundant features in a dataset whereas instance selection aims at the reduction of the number of instances . So far , these two methods have mostly been considered in isolation . In this paper , we present a new algorithm , which we call FIS ( Feature and Instance Selection ) that targets both problems simultaneously in the context of text classificationOur experiments on the Reuters and 20-Newsgroups datasets show that FIS considerably reduces both the number of features and the number of instances . The accuracy of a range of classifiers including Naïve Bayes , TAN and LB considerably improves when using the FIS preprocessed datasets , matching and exceeding that of Support Vector Machines , which is currently considered to be one of the best text classification methods . In all cases the results are much better compared to Mutual Information based feature selection . The training and classification speed of all classifiers is also greatly improved .
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