Abstract
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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© 2004 Springer-Verlag Berlin Heidelberg
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Burduk, R. (2004). Decision Rules for Bayesian Hierarchical Classifier with Fuzzy Factor. In: Soft Methodology and Random Information Systems. Advances in Soft Computing, vol 26. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44465-7_64
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DOI: https://doi.org/10.1007/978-3-540-44465-7_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22264-4
Online ISBN: 978-3-540-44465-7
eBook Packages: Springer Book Archive