The issue of rule generalization has received a great deal of attention in the discrete-valued learning classifier system field. In particular, the accuracy based XCS is the subject of ex-tensive ongoing research. However, the same issue does not appear to have received a similar level of attention in the case of the fuzzy classifier system. This may be due to the difficulty in extending the discrete-valued system operation to the con-tinuous case. The intention of this contribution is to pro-pose an approach to properly develop a fuzzy XCS system
Fuzzy Classifier Systems (FCS) implement a mapping from real numbers to real numbers, through fuzzy ...
AbstractFuzzy Rule-Based Systems are appropriate tools to deal with classification problems due to t...
Abstract: This paper describes the main ideas used in the development of a fuzzy classifier system w...
The issue of finding fuzzy models with an interpretability as good as possible without decreasing th...
Abstract. This paper introduces an approximate fuzzy representation to Fuzzy-UCS, a Michigan-style L...
The fuzzy classifier system (FCS) combines the ideas of fuzzy logic controllers (FLC's) and learning...
We present a class of Learning Classifier Systems that learn fuzzy rule-based models, instead of int...
This paper presents Fuzzy-UCS, a Michigan-style Learn-ing Fuzzy-Classifier System designed for super...
In a fuzzy classifier, the maping “inputs–output” is described by the linguistic 〈If–then〉 rules, th...
This report describes the fuzzy classifier system and a new payoff distribution scheme that performs...
Fuzzy rule-based systems are universal approximators of non-linear functions [1] as multilayer feedf...
Many real-world problems require the development and appli-cation of algorithms that automatically g...
Abstract — Sophisticated fuzzy rule systems are supposed to be flexible, complete, consistent and co...
Several issues arise when we consider building classifiers in general, and fuzzy classifiers in part...
This paper extends our recent work about dropout for the design of Takagi–Sugeno–Kang(TSK) fuzzy cla...
Fuzzy Classifier Systems (FCS) implement a mapping from real numbers to real numbers, through fuzzy ...
AbstractFuzzy Rule-Based Systems are appropriate tools to deal with classification problems due to t...
Abstract: This paper describes the main ideas used in the development of a fuzzy classifier system w...
The issue of finding fuzzy models with an interpretability as good as possible without decreasing th...
Abstract. This paper introduces an approximate fuzzy representation to Fuzzy-UCS, a Michigan-style L...
The fuzzy classifier system (FCS) combines the ideas of fuzzy logic controllers (FLC's) and learning...
We present a class of Learning Classifier Systems that learn fuzzy rule-based models, instead of int...
This paper presents Fuzzy-UCS, a Michigan-style Learn-ing Fuzzy-Classifier System designed for super...
In a fuzzy classifier, the maping “inputs–output” is described by the linguistic 〈If–then〉 rules, th...
This report describes the fuzzy classifier system and a new payoff distribution scheme that performs...
Fuzzy rule-based systems are universal approximators of non-linear functions [1] as multilayer feedf...
Many real-world problems require the development and appli-cation of algorithms that automatically g...
Abstract — Sophisticated fuzzy rule systems are supposed to be flexible, complete, consistent and co...
Several issues arise when we consider building classifiers in general, and fuzzy classifiers in part...
This paper extends our recent work about dropout for the design of Takagi–Sugeno–Kang(TSK) fuzzy cla...
Fuzzy Classifier Systems (FCS) implement a mapping from real numbers to real numbers, through fuzzy ...
AbstractFuzzy Rule-Based Systems are appropriate tools to deal with classification problems due to t...
Abstract: This paper describes the main ideas used in the development of a fuzzy classifier system w...