Abstract
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.
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References
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)
Mitchell, T.M.: Machine Learning. McGraw Hill, New York (1996)
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)
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)
Cohen, W.W., Singer, Y.: Context–Sensitive Learning Methods for Text Categorization. ACM Trans. Inform. Syst. 17(2), 141–173 (1999)
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)
Grossman, D.A.: Ophir Frieder Information Retrieval: Algorithms and Heuristics. Kluwer Academic, Dordrecht (2000)
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)
Wasserman, P.D.: Neural Computing: Theory and Practice. Van Nostrand Reinhold, New York (1989)
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)
Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)
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)
Liming, Z.: Models and applications of Artificial Neural Networks, p. 50. Fudan University, Shanghai (1993)
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)
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© 2006 Springer-Verlag Berlin Heidelberg
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Li, C.H., Park, S.C. (2006). A Novel Algorithm for Text Categorization Using Improved Back-Propagation Neural Network. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_53
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DOI: https://doi.org/10.1007/11881599_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45916-3
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