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Artificial Intelligence
Connectionist learning of belief networks
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On the issue of defuzzification and selection based on a fuzzy set
Fuzzy Sets and Systems
Fuzzy Sets and Systems
A learning procedure to identify weighted rules by neural networks
Fuzzy Sets and Systems
Probabilities, possibilities, and fuzzy sets
Fuzzy Sets and Systems
Propagating imprecise probabilities in Bayesian networks
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Weighted fuzzy production rules
Fuzzy Sets and Systems
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Machine Learning - Special issue on learning with probabilistic representations
IEEE Transactions on Evolutionary Computation
Constraining the optimization of a fuzzy logic controller using anenhanced genetic algorithm
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Learning Bayesian network structures by searching for the best ordering with genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Expert Systems with Applications: An International Journal
Linguistic modelling and information coarsening based on prototype theory and label semantics
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A prototype-based rule inference system incorporating linear functions
Fuzzy Sets and Systems
Fuzzy modeling incorporated with fuzzy d-s theory and fuzzy naive bayes
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Financial time series forecasting with a bio-inspired fuzzy model
Expert Systems with Applications: An International Journal
A novel fuzzy Dempster-Shafer inference system for brain MRI segmentation
Information Sciences: an International Journal
A random set and rule-based regression model incorporating image labels
IUKM'13 Proceedings of the 2013 international conference on Integrated Uncertainty in Knowledge Modelling and Decision Making
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We present a method to identify a fuzzy model from data by using the fuzzy Naive Bayes and a real-valued genetic algorithm. The identification of a fuzzy model is comprised of the extraction of "if-then" rules that is followed by the estimation of their parameters. The involved parameters include those which determine the membership function of fuzzy sets and the certainty factors of fuzzy if-then rules. In our method, as long as the fuzzy partition in the input-output space is given, the certainty factor of each rule is computed with the fuzzy conditional probability of the consequent conditioned on the antecedent by using the fuzzy Naive Bayes, which is a generalization of Naive Bayes. The fuzzy model involves the rules characterized by the highest values of certainty factors. The certainty factor of each rule is the fuzzy conditional probability, and it reflects the inner relationship between the antecedent and the consequent. In order to improve the accuracy of the fuzzy model, the real-valued genetic algorithm is incorporated into our identification process. This process concerns the optimization of the membership functions occurring in the rules. We just involve the parameters of membership function of the fuzzy sets into the real-valued genetic algorithm, since the certainty factor of each rule can be computed automatically. The performance of the model is shown for the backing-truck problem and the prediction of Mackey-Glass time series.