@article {TRAWINSKI20133,
	title = {Multiobjective genetic classifier selection for random oracles fuzzy rule-based classifier ensembles: How beneficial is the additional diversity?},
	journal = {Knowledge-Based Systems},
	volume = {54},
	year = {2013},
	pages = {3 - 21},
	abstract = {Recently we proposed the use of the Random Linear Oracles classical classifier ensemble (CE) design methodology in a fuzzy environment. It derived fuzzy rule-based CEs obtaining an outstanding performance. Random Oracles introduce an additional diversity into the base classifiers improving the accuracy of the entire CE. Meanwhile, the overproduce-and-choose strategy leads to a good accuracy-complexity trade-off. It is based on the generation of a large number of component classifiers and a subsequent selection of the best cooperating subset of them. The current contribution has a twofold aim: (1) Introduce a new Random Oracles approach into the fuzzy rule-based CEs design; (2) Incorporate an evolutionary multi-objective overproduce-and-choose strategy to our approach analyzing the influence of this additional diversity in the final CE performance (focusing on the accuracy). To do so, firstly, we incorporate the two Random Oracle variants into the fuzzy rule-based CE framework. Then, we use NSGA-II to provide a specific component classifier selection driven by three different criteria. Exhaustive experiments are carried out over 29 UCI and KEEL datasets with high complexity (considering both the number of attributes as well as the number of examples) showing the good performance of the proposed approach.},
	keywords = {Bagging, Diversity measures, Evolutionary multiobjective optimization, Fuzzy rule-based classifier ensembles, Genetic classifier selection, High complexity datasets, NSGA-II, Random oracles},
	issn = {0950-7051},
	doi = {https://doi.org/10.1016/j.knosys.2013.08.006},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705113002360},
	author = {Krzysztof Trawi{\'n}ski and O. Cord{\'o}n and Arnaud Quirin and Luciano S{\'a}nchez}
}
@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}
}
@conference {4626687,
	title = {On the Use of Bagging, Mutual Information-Based Feature Selection and Multicriteria Genetic Algorithms to Design Fuzzy Rule-Based Classification Ensembles},
	booktitle = {2008 Eighth International Conference on Hybrid Intelligent Systems},
	year = {2008},
	month = {Sep.},
	pages = {549-554},
	abstract = {In this contribution we explore the combination of bagging with random subspace and two variants of Battiti{\textquoteright}s mutual information feature selection methods to design fuzzy rule-based classification system ensembles. Besides, we consider a multicriteria genetic algorithm guided by the training error to select the component classifiers, in order to look for appropriate accuracy-complexity trade-offs in the final multiclassifier.},
	keywords = {Bagging, classification, Classification algorithms, fuzzy rule-based classification ensembles, fuzzy set theory, Gallium, genetic algorithms, Glass, multicriteria genetic algorithms, mutual information-based feature selection, Sonar, Training, Vehicles},
	doi = {10.1109/HIS.2008.147},
	author = {O. Cord{\'o}n and A. Quirin and L. S{\'a}nchez}
}
