A New Hybrid Method for Text Feature Selection Through Combination of Relative Discrimination Criterion and Ant Colony Optimization

Abstract

Text categorization plays a significant role in many information management tasks. Due to the increasing volume of documents on the Internet, automated text categorization has been more considered for classifying documents in pre-defined categories. A major problem of text categorization is the high dimensionality of feature space. Most of the features are irrelevant and redundant impacting the classifier performance. Hence, feature selection is used to reduce the high dimensionality of feature space and increase classification efficiency. In this paper, we proposed a hybrid two-stage method for text feature selection based on Relative Discrimination Criterion (RDC) and Ant Colony Optimization (ACO). To this end, we applied RDC method, at first, in order to rank features based on their values. Features, then, which their values are lower than a threshold are removed from the feature set. In the second stage, as a wrapper method, an ACO-based feature selection method is applied, to select redundant or irrelevant features that have not been removed in the first stage. Finally, to assess the proposed methods, we have conducted several experiments on different datasets to indicate the superiority of our proposed algorithm. We aim to propose a hybrid approach which is computationally more efficient in much the same way as it is more accurate compared to the other embedded or wrapper methods. The obtained results endorse that the proposed method is of remarkable performance in text feature selection.