Decision Rules for Bayesian Hierarchical Classifier with Fuzzy Factor

Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 26)


The paper deals with the decision rules for a Bayesian hierarchical classifier based on a decision-tree scheme. For given tree skeleton and features to be used, the optimal (Bayes) decision rules (strategy) at each non-terminal node are presented. The case has been considered when a loss (utility) function is described using fuzzy numbers. The globally optimal Bayes strategy has been calculated for the case when the loss function depends on the node of the decision tree. The model is based on the notion of fuzzy random variable and also on crisp ranking method for fuzzy numbers. The obtained result is presented as a calculation example.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  1. 1.Wroclaw University of TechnologyWroclawPoland

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