Text categorization using feature projections

Authors:
Youngjoong Ko;Jungyun Seo
Affiliations:
Sogang University, Seoul, Korea;Sogang University, Seoul, Korea
Venue:
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Year:
2002

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Abstract

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.