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Learning Fuzzy Decision Rules

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Fuzzy Sets in Approximate Reasoning and Information Systems

Part of the book series: The Handbooks of Fuzzy Sets Series ((FSHS,volume 5))

Abstract

In this chapter a review of existing methods to learn fuzzy decision rules is done. Two kinds of learning methods exist. The first kind deals with the determination of the structure of the fuzzy decision rules. Fuzzy propositions (Vk is Ak) have to be chosen to compose premises and conclusions of such rules. The second kind of method is concerned with the tuning of the membership functions associated with the fuzzy propositions that appear in premises and conclusions of a given set of fuzzy decision rules.

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Authors and Affiliations

  1. LIP6, Université Pierre et Marie Curie, Case 169, 4 place Jussieu, 75252, Paris Cedex 5, France

    Bernadette Bouchon-Meunier & Christophe Marsala

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  1. Bernadette Bouchon-Meunier
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  2. Christophe Marsala
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Editors and Affiliations

  1. University of West Florida, USA

    James C. Bezdek

  2. IRIT, CNRS & University of Toulouse III, France

    Didier Dubois  & Henri Prade  & 

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Bouchon-Meunier, B., Marsala, C. (1999). Learning Fuzzy Decision Rules. In: Bezdek, J.C., Dubois, D., Prade, H. (eds) Fuzzy Sets in Approximate Reasoning and Information Systems. The Handbooks of Fuzzy Sets Series, vol 5. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5243-7_5

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  • DOI: https://doi.org/10.1007/978-1-4615-5243-7_5

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