Creating prototypes for fast classification in Dempster-Shafer clustering
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We develop a classification method for incoming pieces of evidence in Dempster-Shafer theory. This methodology is based on previous work with clustering and specification of originally nonspecific evidence. This methodology is here put in order for fast classification of future incoming pieces of evidence by comparing them with prototypes representing the clusters, instead of making a full clustering of all evidence. This method has a computational complexity of O(M·N) for each new piece of evidence, where M is the maximum number of subsets and N is the number of prototypes chosen for each subset. That is, a computational complexity independent of the total number of previously arrived pieces of evidence. The parameters M and N are typically fixed and domain dependent in any application.
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- 1.J. Schubert, On nonspecific evidence, Int. J. Intell. Syst. 8(6) (1993) 711–725.Google Scholar
- 2.J. Schubert, Specifying nonspecific evidence, Int. J. Intell. Syst. 11(8) (1996) 525–563.Google Scholar
- 3.J. Schubert, Finding a posterior domain probability distribution by specifying nonspecific evidence, Int. J. Uncertainty, Fuzziness and Knowledge-Based Syst. 3(2) (1995) 163–185.Google Scholar
- 4.J. Schubert, Cluster-based specification techniques in Dempster-Shafer theory, in Symbolic and Quantitative Approaches to Reasoning and Uncertainty, C. Froidevaux and J. Kohlas (Eds.), Proc. European Conf. Symbolic and Quantitative Approaches to Reasoning and Uncertainty, Springer-Verlag (LNAI 946), Berlin, 1995, pp. 392–404.Google Scholar
- 5.J. Schubert, Cluster-based specification techniques in Dempster-Shafer theory for an evidential intelligence analysis of multiple target tracks, Ph.D. thesis, TRITA-NA-9410, Royal Institute of Technology, Stockholm, Sweden, 1994, ISBN 91-7170-801-4.Google Scholar
- 6.G. Shafer, A Mathematical Theory of Evidence. Princeton University Press, Princeton, 1976.Google Scholar
- 7.T. Denœux, A k-nearest neighbor classification rule based on Dempster-Shafer theory, IEEE Trans. Syst. Man Cyber. 25(5) (1995) 804–813.Google Scholar