@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}
}
@conference {4484560,
	title = {A first study on bagging fuzzy rule-based classification systems with multicriteria genetic selection of the component classifiers},
	booktitle = {2008 3rd International Workshop on Genetic and Evolving Systems},
	year = {2008},
	month = {March},
	pages = {11-16},
	keywords = {Bagging, bagging fuzzy rule-based classification system, Boosting, component classifier, Design methodology, evolutionary computation, fuzzy set theory, Fuzzy systems, genetic algorithms, heuristic fuzzy classification rule generation method, Humans, knowledge based systems, learning (artificial intelligence), machine learning, multicriteria genetic algorithm, pattern classification, Proposals, Scalability},
	doi = {10.1109/GEFS.2008.4484560},
	author = {O. Cord{\'o}n and A. Quirin and L. S{\'a}nchez}
}
@article {4286977,
	title = {Advocating the Use of Imprecisely Observed Data in Genetic Fuzzy Systems},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {15},
	number = {4},
	year = {2007},
	month = {Aug},
	pages = {551-562},
	abstract = {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.},
	keywords = {Bayesian methods, data handling, Design for experiments, discriminant function, fuzzy fitness function, fuzzy rule-based classifiers, fuzzy rule-based models, fuzzy rules, fuzzy set theory, Fuzzy sets, Fuzzy systems, genetic algorithm, genetic algorithms, genetic fuzzy classifiers, Genetic Fuzzy Systems, learning (artificial intelligence), linguistic interpretation, machine learning, machine learning theory, Parametric statistics, pattern classification, Robustness, scalar fitness function, statistical analysis, Stochastic resonance, vague data},
	issn = {1063-6706},
	doi = {10.1109/TFUZZ.2007.895942},
	author = {L. S{\'a}nchez and I. Couso}
}
