Higher order models for fuzzy random variables
Fuzzy Sets and Systems
International Journal of Approximate Reasoning
Evolving Fuzzy Rules with UCS: Preliminary Results
Learning Classifier Systems
Taximeter verification using imprecise data from GPS
Engineering Applications of Artificial Intelligence
Genetic learning of fuzzy rules based on low quality data
Fuzzy Sets and Systems
A Minimum-Risk Genetic Fuzzy Classifier Based on Low Quality Data
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Fuzzy-UCS: a Michigan-style learning fuzzy-classifier system for supervised learning
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
On the variability of the concept of variance for fuzzy random variables
IEEE Transactions on Fuzzy Systems
Construction of a neuron-fuzzy classification model based on feature-extraction approach
Expert Systems with Applications: An International Journal
Diagnosis of dyslexia with low quality data with genetic fuzzy systems
International Journal of Approximate Reasoning
An adaptive knowledge evolution strategy for finding near-optimal solutions of specific problems
Expert Systems with Applications: An International Journal
Upper and lower probabilities induced by a fuzzy random variable
Fuzzy Sets and Systems
Upper and lower probabilities induced by a fuzzy random variable
Fuzzy Sets and Systems
Mark-recapture techniques in statistical tests for imprecise data
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Analysing the low quality of the data in lighting control systems
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Expert Systems with Applications: An International Journal
Inner and outer fuzzy approximations of confidence intervals
Fuzzy Sets and Systems
Information Sciences: an International Journal
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In our opinion, and in accordance with current literature, the precise contribution of genetic fuzzy systems to the corpus of the machine learning theory has not been clearly stated yet. In particular, we question the existence of a set of problems for which the use of fuzzy rules, in combination with genetic algorithms, produces more robust models, or classifiers that are inherently better than those arising from the Bayesian point of view. We will show that this set of problems actually exists, and comprises interval and fuzzy valued datasets, but it is not being exploited. Current genetic fuzzy classifiers deal with crisp classification problems, where the role of fuzzy sets is reduced to give a parametric definition of a set of discriminant functions, with a convenient linguistic interpretation. Provided that the customary use of fuzzy sets in statistics is vague data, we propose to test genetic fuzzy classifiers over imprecisely measured data and design experiments well suited to these problems. The same can be said about genetic fuzzy models: the use of a scalar fitness function assumes crisp data, where fuzzy models, a priori, do not have advantages over statistical regression.