Text Categorization using Feature Projections

This paper proposes a new approach for text categorization, based on a feature projection technique. In our approach, training data are represented as the projections of training documents on each feature. The voting for a classification is processed on the basis of individual feature projections. The final classification of test documents is determined by a majority voting from the individual classifications of each feature. Our empirical results show that the proposed approach, Text Categorization using Feature Projections (TCFP), outperforms k-NN, Rocchio, and Naïve Bayes. Most of all, TCFP is about one hundred times faster than k-NN. Since TCFP algorithm is very simple, its implementation and training process can be done very easily. For these reasons, TCFP can be a useful classifier in the areas, which need a fast and high-performance text categorization task.