scholarly journals Fuzzy Rule-Based Classification Systems for multi-class problems using binary decomposition strategies: On the influence of n-dimensional overlap functions in the Fuzzy Reasoning Method

2016 ◽  
Vol 332 ◽  
pp. 94-114 ◽  
Author(s):  
Mikel Elkano ◽  
Mikel Galar ◽  
Jose Sanz ◽  
Humberto Bustince
Keyword(s):  
Fuzzy Rule ◽  
Fuzzy Reasoning ◽  
Classification Systems ◽  
Rule Based ◽  
Binary Decomposition ◽  
Decomposition Strategies

scholarly journals Using the Choquet Integral in the Fuzzy Reasoning Method of Fuzzy Rule-Based Classification Systems

Axioms ◽  
2013 ◽  
Vol 2 (2) ◽  
pp. 208-223 ◽  
Author(s):  
Edurne Barrenechea ◽  
Humberto Bustince ◽  
Javier Fernandez ◽  
Daniel Paternain ◽  
José Sanz
Keyword(s):  
Choquet Integral ◽  
Fuzzy Rule ◽  
Fuzzy Reasoning ◽  
Classification Systems ◽  
Rule Based

An Alternative to Power Measure for Fuzzy Rule-Based Classification Systems

2020 ◽  
pp. 231-244
Author(s):  
Frederico B. Tiggemann ◽  
Bryan G. Pernambuco ◽  
Giancarlo Lucca ◽  
Eduardo N. Borges ◽  
Helida Santos ◽  
...  
Keyword(s):  
Fuzzy Rule ◽  
Classification Systems ◽  
Rule Based ◽  
Power Measure

Fuzzy Rule Interpolation

2011 ◽  
pp. 728-733 ◽  
Author(s):  
Szilveszter Kovács
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Fuzzy Reasoning ◽  
Fuzzy Relation ◽  
Rule Base ◽  
Rule Based ◽  
Fuzzy Rule Base ◽  
Current Observation ◽  
Rule Bases ◽  
Takagi Sugeno

The “fuzzy dot” (or fuzzy relation) representation of fuzzy rules in fuzzy rule based systems, in case of classical fuzzy reasoning methods (e.g. the Zadeh-Mamdani- Larsen Compositional Rule of Inference (CRI) (Zadeh, 1973) (Mamdani, 1975) (Larsen, 1980) or the Takagi - Sugeno fuzzy inference (Sugeno, 1985) (Takagi & Sugeno, 1985)), are assuming the completeness of the fuzzy rule base. If there are some rules missing i.e. the rule base is “sparse”, observations may exist which hit no rule in the rule base and therefore no conclusion can be obtained. One way of handling the “fuzzy dot” knowledge representation in case of sparse fuzzy rule bases is the application of the Fuzzy Rule Interpolation (FRI) methods, where the derivable rules are deliberately missing. Since FRI methods can provide reasonable (interpolated) conclusions even if none of the existing rules fires under the current observation. From the beginning of 1990s numerous FRI methods have been proposed. The main goal of this article is to give a brief but comprehensive introduction to the existing FRI methods.

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