A multi-criteria decision making approach in feature selection for enhancing text categorization

This paper considers the problem of feature selection in text categorization. Previous works in feature selection often used a filter model in which features, after ranked by a measure, are selected based on a given threshold. In this paper, we present a novel approach to feature selection based on multi-criteria decision making of each feature. Instead of only one criterion, multi-criteria of a feature are used; and a procedure based on each threshold of the criterion is proposed. This framework seems to be suitable for text data and can be applied to feature selection in text categorization. Experimental results on Reuters-21578 benchmark data show that our approach has a promising scheme and enhances the performance of a text categorization system.