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Linguistic recognition system based on approximate reasoning
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Genetic algorithms + data structures = evolution programs (3rd ed.)
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On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
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A new approach for ranking fuzzy numbers by distance method
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International Journal of Approximate Reasoning
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On multi-class cost-sensitive learning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Area ranking of fuzzy numbers based on positive and negative ideal points
Computers & Mathematics with Applications
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
A proposal on reasoning methods in fuzzy rule-based classification systems
International Journal of Approximate Reasoning
Information Sciences: an International Journal
GP-based secondary classifiers
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Fuzzy linguistic decision to provide alternatives to market mechanism strategies
Expert Systems with Applications: An International Journal
Mark-recapture techniques in statistical tests for imprecise data
International Journal of Approximate Reasoning
Multi-objective optimization of problems with epistemic uncertainty
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
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IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
Nearest interval, triangular and trapezoidal approximation of a fuzzy number preserving ambiguity
International Journal of Approximate Reasoning
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Cost-sensitive classification is based on a set of weights defining the expected cost of misclassifying an object. In this paper, a Genetic Fuzzy Classifier, which is able to extract fuzzy rules from interval or fuzzy valued data, is extended to this type of classification. This extension consists in enclosing the estimation of the expected misclassification risk of a classifier, when assessed on low quality data, in an interval or a fuzzy number. A cooperative-competitive genetic algorithm searches for the knowledge base whose fitness is primal with respect to a precedence relation between the values of this interval or fuzzy valued risk. In addition to this, the numerical estimation of this risk depends on the entrywise product of cost and confusion matrices. These have been, in turn, generalized to vague data. The flexible assignment of values to the cost function is also tackled, owing to the fact that the use of linguistic terms in the definition of the misclassification cost is allowed.