Abstract
This chapter describes an alternative to the Bayesian approach to target classification that is based on possibility theory. A possibilistic classifier minimizes the maximum cost of the classification decision taking into account the a posteriori possibilities of the target classes given the measured target attributes. The advantage of a possibilistic classifier when compared with a Bayesian classifier is that it requires only an ordinal ranking of the costs associated with the classification decisions and the uncertainty about the target class. Owing to its qualitative character, a possibilistic classifier is less sensitive to inaccuracies in a priori knowledge than a Bayesian classifier at the expense of a degraded performance in situations where accurate a priori knowledge is available. This robustness of the possibilistic classifier to inaccuracies in a priori knowledge is demonstrated in a case study where an average cost criterion is used to compare the performance of a possibilistic and a Bayesian classifier. It is shown that when the characteristics of the measured target attributes deviate strongly from the expected characteristics, the possibilistic classifier provides a lower average cost than a Bayesian classifier.
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© 2000 Springer-Verlag Berlin Heidelberg
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Huizing, A.G., Groen, F.C.A. (2000). A Possibilistic Approach to Target Classification. In: Ruan, D. (eds) Fuzzy Systems and Soft Computing in Nuclear Engineering. Studies in Fuzziness and Soft Computing, vol 38. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1866-6_19
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DOI: https://doi.org/10.1007/978-3-7908-1866-6_19
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2466-7
Online ISBN: 978-3-7908-1866-6
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