Fuzzy Rule Base Generation through Genetic Algorithms and Bayesian Classifiers A Comparative Approach

  title={Fuzzy Rule Base Generation through Genetic Algorithms and Bayesian Classifiers A Comparative Approach},
  author={Marcos E. Cintra and Heloisa A. Camargo and Estevam R. Hruschka and Maria do Carmo Nicoletti},
  journal={Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007)},
The definition of the fuzzy rule base is one of the most important and difficult tasks when designing fuzzy systems. This paper discusses the results of two different hybrid methods investigated earlier, for the automatic generation of fuzzy rules from numerical data. One of the methods proposes the creation of fuzzy rule bases using genetic algorithms in association with a heuristic for preselecting candidate rules. The other, named Bayes fuzzy, induces a Bayes classifier using a dataset… CONTINUE READING


Publications referenced by this paper.
Showing 1-10 of 26 references

Fuzzy Rules Generation using Genetic Algorithms with Self-adaptive Selection

2007 IEEE International Conference on Information Reuse and Integration • 2007
View 3 Excerpts

Fuzzy rules generation with preselection of candidate rules

M. E. Cintra, H. A. Camargo
(submitted for publication; in Portuguese), • 2007
View 2 Excerpts

Focusing on Interpretability and Accuracy of a Genetic Fuzzy System

The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05. • 2005
View 1 Excerpt

Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining

H. Ishibuchi, T. Yamamoto
Fuzzy Sets and Systems, • 2004
View 1 Excerpt

Special issue on genetic fuzzy systems

O. Cordón, F. Herrera, F. Gomide, F. Hoffmann, L. Magdalena
Fuzzy Sets and Systems, • 2004
View 2 Excerpts

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