Abstract
In this paper, we describe a way for modelling a generalization process involved in the combination of multiple classification systems as an evidential reasoning process. We first propose a novel structure for representing multiple pieces of evidence de‘rived from multiple classifiers. This structure is called a focal element triplet. We then present a method for combining multiple pieces of evidence by using Dempster’s rule of combination. The advantage of the novel structure is that it not only facilitates the distinguishing of trivial focal elements from important ones, but it also reduces the effective computation-time from exponential as in the conventional process of combining multiple pieces of evidence to linear. In consequence, this allows Dempster’s rule of combination to be implemented in a widened range of applications.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
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)
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)
Mitchell, T.: Mitchell. Machine Learning. McGraw-Hill, New York (1997)
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
Bi, Y.: Combining Multiple Classifiers for Text Categorization using Dempster’s rule of combination. Internal report (2004)
Bi, Y., Bell, D.: Specification of Dempster-Shafer’s Uncertainty Reasoning Engine. The ICONS deliverable 21 (2003)
Barnett, J.A.: Computational methods for a mathematical theory of evidence. In: Proceedings of Seventh Joint Conference of Artificial Intelligence (IJCAI 1981), pp. 868–875 (1981)
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., Guan, J. (2004). Combining Evidence from Classifiers in Text Categorization. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30134-9_70
Download citation
DOI: https://doi.org/10.1007/978-3-540-30134-9_70
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23205-6
Online ISBN: 978-3-540-30134-9
eBook Packages: Springer Book Archive