Abstract
In this paper, we present an investigation into the combination of four different classification methods for text categorization using Dempster’s rule of combination. These methods include the Support Vector Machine, kNN (nearest neighbours), kNN model-based approach (kNNM), and Rocchio methods. We first present an approach for effectively combining the different classification methods. We then apply these methods to a benchmark data collection of 20-newsgroup, individually and in combination. Our experimental results show that the performance of the best combination of the different classifiers on the 10 groups of the benchmark data can achieve 91.07% classification accuracy, which is 2.68% better than that of the best individual method, SVM, on average.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Xu, L., Krzyzak, A., Suen, C.Y.: Several Methods for Combining Multiple Classifiers and Their Applications in Handwritten Character Recognition. IEEE Trans. on System, Man and Cybernetics 22(3), 418–435 (1992)
Denoeux, T.: A neural network classifier based on Dempster-Shafer theory. IEEE transactions on Systems, Man and Cybernetics A 30(2), 131–150 (2000)
Yang, Y., Ault, T., Pierce, T.: Combining multiple learning strategies for effective cross validation. In: The Seventeenth International Conference on Machine Learning (ICML 2000), pp. 1167–1182 (2000)
Ho, T.K.: Multiple Classifier Combination: Lessons and Next Steps. In: Ho, T.K., Kandel, A., Bunke, H. (eds.) Hybrid Methods in Pattern Recognition, pp. 171–198. World Scientific, Singapore (2002)
Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys 34(1) (2002)
Li, Y.H., Jain, A. k.: Classification of Text Documents. The Computer Journal 41(8), 537–546 (1998)
Larkey, L.S., Croft, W.B.: Combining classifiers in text categorization. In: Proceedings of SIGIR 1996, 19th ACM International Conference on Research and Development in Information Retrieval, pp. 289–297 (1996)
Guan, J.W., Bell, D.: Evidence Theory and its applications, vol. 1,2 (1991)
Bi, Y., Bell, D., Guan, J.W.: Combining Evidence from Classifiers in Text Categorization. To appear in 8th International Conference on Knowledge-Based Intelligent Information & Engineering Systems (2004)
Bi, Y.: Combining Multiple Classifiers for Text Categorization using Dempster- Shafer Theory of Evidence. Internal report (2004)
Shi, S.: On Reasoning with Uncertainty and Belief Change. PhD thesis. University of Ulster (1995)
van Rijsbergen, C.J.: Information Retrieval, 2nd edn., Butterworths (1979)
Joachims, T.: A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. In: The Fourteen International Conference on Machine Learning, ICML 1997 (1997)
Salton, G., Allan, J., Buckley, C., Singhal, A.: Automatic Analysis, Theme Generation, and Summarization of Machine-Readable Texts. Science 264, 1421–1426 (1994)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Guo, G., Wang, H., Bell, D.J., Bi, Y., Greer, K.: KNN model-based approach in classification. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds.) CoopIS 2003, DOA 2003, and ODBASE 2003. LNCS, vol. 2888, pp. 986–996. Springer, Heidelberg (2003)
Yang, Y.: A study on thresholding strategies for text categorization. In: Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2001), pp. 137–145 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bi, Y., Bell, D., Wang, H., Guo, G., Greer, K. (2004). Combining Multiple Classifiers Using Dempster’s Rule of Combination for Text Categorization. In: Torra, V., Narukawa, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2004. Lecture Notes in Computer Science(), vol 3131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27774-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-27774-3_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22555-3
Online ISBN: 978-3-540-27774-3
eBook Packages: Springer Book Archive