A Novel Algorithm for Text Categorization Using Improved Back-Propagation Neural Network

  • Cheng Hua Li
  • Soon Cheol Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)


This paper describes a novel adaptive learning approach for text categorization based on a Back-propagation neural network (BPNN). The BPNN has been widely used in classification and pattern recognition; however it has some generally acknowledged defects, which usually originate from some morbidity neurons. In this paper, we introduce an improved BPNN that can overcome these defects and rectify the morbidity neurons. We tested the improved model on the standard Reuter-21578, and the result shows that the proposed model is able to achieve high categorization effectiveness as measured by the precision, recall and F-measure.


Hide Node Learning Phase Text Categorization Term Weight Training Document 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Yang, Y., Liu, X.: A Re-examination of Text Categorization Methods. In: Proceedings of SIGIR 1999, 22nd ACM International Conference on Research and Development in Information Retrieval, pp. 42–49 (1999)Google Scholar
  2. 2.
    Mitchell, T.M.: Machine Learning. McGraw Hill, New York (1996)MATHGoogle Scholar
  3. 3.
    Rocchio Jr., J.: JRelevance Feedback in Information Retrieval. In: Salton, G. (ed.) The SMART Retrieval System: Experiments in Automatic Document Processing, Prentice-Hall, Inc., Englewood Cliffs (1971)Google Scholar
  4. 4.
    Joachims, T.: A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Test Categorization. In: Proceedings of ICML 1997, 14th International Conference on Machine Learning, pp. 143–151 (1997)Google Scholar
  5. 5.
    Cohen, W.W., Singer, Y.: Context–Sensitive Learning Methods for Text Categorization. ACM Trans. Inform. Syst. 17(2), 141–173 (1999)CrossRefGoogle Scholar
  6. 6.
    Ruiz, M.E., Srinivasan, P.: Hierarchical Neural Networks for Text Categorization. In: Proceedings of SIGIR 1999, 22nd ACM International Information Retrieval, pp. 281–282 (1999)Google Scholar
  7. 7.
    Grossman, D.A.: Ophir Frieder Information Retrieval: Algorithms and Heuristics. Kluwer Academic, Dordrecht (2000)Google Scholar
  8. 8.
    Wu, W., Feng, G., Li, Z., Xu, Y.: Deterministic Convergence of an Online Gradient Method for BP Neural Networks. IEEE Transactions on Neural Networks 16(3) (2005)Google Scholar
  9. 9.
    Wasserman, P.D.: Neural Computing: Theory and Practice. Van Nostrand Reinhold, New York (1989)Google Scholar
  10. 10.
    Plagianakos, V.P., Vrahatis, M.N.: Training Neural Networks with Threshold Activation Functions and Constrained Integer Weights. In: IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2000), vol. 5, p. 5161 (2000)Google Scholar
  11. 11.
    Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)Google Scholar
  12. 12.
    Lam, S.L.Y., Lee, D.L.: Feature Reduction for Neural Network Based Text Categorization. In: 6th International Conference on Database Systems for Advanced Applications (DASFAA 1999), p. 195 (1999)Google Scholar
  13. 13.
    Liming, Z.: Models and applications of Artificial Neural Networks, p. 50. Fudan University, Shanghai (1993)Google Scholar
  14. 14.
    Lewis, D.D., Gale, W.A.: A Sequential Algorithm for Training Text Classifiers. In: SIGIR 1994 Proceedings of the 17th Annual International ACM SIGIR Conference, London, pp. 3–12 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Cheng Hua Li
    • 1
  • Soon Cheol Park
    • 1
  1. 1.Division of Electronics and Information EngineeringChonbuk National UniversityJeonju, JeonbukKorea

Personalised recommendations