A Possibilistic Approach to Target Classification

  • Albert G. Huizing
  • Frans C. A. Groen
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 38)


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.


Likelihood Function Average Cost Target Class Bayesian Classifier Possibility Distribution 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Albert G. Huizing
    • 1
  • Frans C. A. Groen
    • 2
  1. 1.TNO Physics and Electronics LaboratoryThe HagueThe Netherlands
  2. 2.Department of Computer ScienceUniversity of AmsterdamAmsterdamThe Netherlands

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