Generating Fuzzy Rules from Ensemble of Relational Fuzzy Systems (bibtex)
by Rafał Scherer
Abstract:
There are many machine learning systems developed so far. Fuzzy systems along with neural network are most commonly used learning systems. Researchers mainly use Mamdani (linguistic) and Takagi Sugeno fuzzy systems and in the paper relational neuro-fuzzy systems are used as systems constituting boosting ensemble of classifiers. We propose a method for joining rule bases from the fuzzy systems into one fuzzy rule base. The ensemble performs very well on known benchmarks.
Reference:
R. Scherer, "Generating Fuzzy Rules from Ensemble of Relational Fuzzy Systems", in Recent Advances in Control and Automation, K. Malinowski, L. Rutkowski, Eds., Academic Publishing House EXIT, 2008, pp. 438-445.
Bibtex Entry:
@INCOLLECTION{Scherer_KKA2008,
  title = {Generating Fuzzy Rules from Ensemble of Relational Fuzzy Systems},
  pages = {438-445},
  booktitle = {Recent Advances in Control and Automation},
  publisher = {Academic Publishing House EXIT},
  year = {2008},
  editor = {K. Malinowski and L. Rutkowski},
  author = {Rafał Scherer},
  abstract = {There are many machine learning systems developed so far. Fuzzy systems
	along with neural network are most commonly used learning systems.
	Researchers mainly use Mamdani (linguistic) and Takagi Sugeno fuzzy
	systems and in the paper relational neuro-fuzzy systems are used
	as systems constituting boosting ensemble of classifiers. We propose
	a method for joining rule bases from the fuzzy systems into one fuzzy
	rule base. The ensemble performs very well on known benchmarks.},
}
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