@conference {5454152,
	title = {Introducing a genetic fuzzy linguistic combination method for bagging fuzzy rule-based multiclassification systems},
	booktitle = {2010 4th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)},
	year = {2010},
	month = {March},
	pages = {75-80},
	abstract = {Many different fuzzy aggregation operators have been successfully used to combine the outputs provided by the individual classifiers in a multiclassification system. However, up to our knowledge, the use of fuzzy combination methods composed of a fuzzy system is less extended. By using a fuzzy linguistic rule-based classification system as a combination method, the resulting classifier ensemble would show a hierarchical structure and the operation of the latter component would be transparent to the user. Moreover, for the specific case of fuzzy multiclassification systems, the new approach could also become a smart way to allow fuzzy classifiers to deal with high dimensional problems avoiding the curse of dimensionality. The present contribution establishes the first basis in this direction by introducing a genetic fuzzy system-based framework to build the fuzzy linguistic combination method for a bagging fuzzy multiclassification system.},
	keywords = {Bagging, bagging fuzzy multiclassification system, Classification tree analysis, classifier ensemble, Computer science, Decision trees, fuzzy aggregation operators, fuzzy linguistic combination methods, fuzzy linguistic rule-based system, Fuzzy reasoning, fuzzy set theory, Fuzzy systems, genetic algorithms, genetic fuzzy-based system, Genetics, knowledge based systems, learning (artificial intelligence), machine learning, Neural networks, pattern classification},
	doi = {10.1109/GEFS.2010.5454152},
	author = {L. S{\'a}nchez and O. Cord{\'o}n and A. Quirin and K. Trawinski}
}
@article {843495,
	title = {Interval-valued GA-P algorithms},
	journal = {IEEE Transactions on Evolutionary Computation},
	volume = {4},
	number = {1},
	year = {2000},
	month = {April},
	pages = {64-72},
	abstract = {When genetic programming (GP) methods are applied to solve symbolic regression problems, we obtain a point estimate of a variable, but it is not easy to calculate an associated confidence interval. We designed an interval arithmetic-based model that solves this problem. Our model extends a hybrid technique, the GA-P method, that combines genetic algorithms and genetic programming. Models based on interval GA-P can devise an interval model from examples and provide the algebraic expression that best approximates the data. The method is useful for generating a confidence interval for the output of a model, and also for obtaining a robust point estimate from data which we know to contain outliers. The algorithm was applied to a real problem related to electrical energy distribution. Classical methods were applied first, and then the interval GA-P. The results of both studies are used to compare interval GA-P with GP, GA-P, classical regression methods, neural networks, and fuzzy models.},
	keywords = {Arithmetic, Computer science, confidence interval, electrical energy distribution, Fuzzy neural networks, Fuzzy systems, genetic algorithms, Genetic programming, Neural networks, point estimate, Robustness, statistical analysis, symbol manipulation, symbolic regression},
	issn = {1089-778X},
	doi = {10.1109/4235.843495},
	author = {L. S{\'a}nchez}
}
