Target Classification Using PAS and Evidence Theory

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This paper presents a novel Dempster-Shafer evidence construction approach for aircraft aim recognition. The prior-probability of the properties of aircraft was used for establishing a probabilistic argumentation system. Dempster-Shafer evidence was constructed by assumption-based reasoning. Therefore, additional information could be provided to the classification of the data fusion system. Experiments on artificial and real data demonstrated that the proposed method could improve the classification results.

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3728-3733

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August 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, Princeton, (1967).

Google Scholar

[2] T. Denoeux, A k-nearest neighbor classification rule based on Dempster–Shafer theory, IEEE Transactions on Systems, Man and Cybernetics , vol. 25, 1995, p.804–813.

DOI: 10.1109/21.376493

Google Scholar

[3] T. Denoeux, A neural network classifier based on Dempster–Shafer theory, IEEE Transactions on Systems, Man and Cybernetics A, vol. 30, 2000, p.131–150.

DOI: 10.1109/3468.833094

Google Scholar

[4] T. Denoeux, L.M. Zouhal, Handling possibilistic labels in pattern classification using evidential reasoning, Fuzzy Sets and Systems, vol. 122, 2001, p.47–62.

DOI: 10.1016/s0165-0114(00)00086-5

Google Scholar

[5] R. Haenni, J. Kohlas, N. Lehmann, Probabilistic argumentation systems, in: J. Kohlas, S. Moral (Eds. ), Algorithms for Uncertainty and Defeasible Reasoning, Handbook of Defeasible Reasoning and Uncertainty Management Systems, vol. 5, Kluwer, Dordrecht, (2000).

DOI: 10.1007/978-94-017-1737-3_6

Google Scholar

[6] J. Kohlas, R. Haenni, S. Moral, Propositional information systems, Journal of Logic and Computation, vol. 9, 1999, p.651–681.

Google Scholar

[7] R. Haenni, Probabilistic argumentation, Journal of Applied Logic, vol. 7, 2009, p.155–176.

Google Scholar

[8] P. Smets, R. Kennes, The transferable belief model, Art. Intell, vol. 66, 1994, p.191–234.

DOI: 10.1016/0004-3702(94)90026-4

Google Scholar

[9] P. Smets, Practical uses of belief functions, in: K.B. Laskey, H. Prade (Eds. ), Uncertainty in Artificial Intelligence, vol. 15, Stockholm, Sweden, (1999).

Google Scholar

[10] P. Smets, Belief functions: the disjunctive rule of combination and the generalized Bayesian theorem, International Journal of Approximate Reasoning, vol. 9, 1993, p.1–35.

DOI: 10.1016/0888-613x(93)90005-x

Google Scholar

[11] B.R. Cobb, P.P. Shenoy, On the plausibility transformation method for translating belief function models to probability models, Int. J. Approx. Reason, vol. 41, 2006, p.314–330.

DOI: 10.1016/j.ijar.2005.06.008

Google Scholar

[12] P. Smets, Decision making in the TBM: the necessity of the pignistic transformation, Int. J. Approx. Reason, vol. 38, 2005, p.133–147.

DOI: 10.1016/j.ijar.2004.05.003

Google Scholar

[13] J. Kohlas, P.A. Monney, A Mathematical Theory of Hints, Lecture Notes in Economics and Mathematical Systems, vol. 425, Springer, Berlin, (1995).

DOI: 10.1007/978-3-662-01674-9

Google Scholar