@conference {6622418,
	title = {CI-LQD: A software tool for modeling and decision making with Low Quality Data},
	booktitle = {2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2013},
	month = {July},
	pages = {1-8},
	abstract = {The software tool CI-LQD (Computational Intelligence for Low Quality Data) is introduced in this paper. CI-LQD is an ongoing project that includes a lightweight open source software that has been designed with scientific and teaching purposes in mind. The main usefulness of the software is to automate the calculations involved in the statistical comparisons of different algorithms, with both numerical and graphical techniques, when the available information is interval-valued, fuzzy, incomplete or otherwise vague. A growing catalog of evolutionary algorithms for learning classifiers, models and association rules, along with their corresponding data conditioning and preprocessing techniques, is included. A demonstrative example of the tool is described that illustrates the capabilities of the software.},
	keywords = {Algorithm design and analysis, Artificial intelligence, Association rules, CI-LQD, Computational intelligence, data conditioning, data mining, data preprocessing technique, Databases, Decision making, evolutionary algorithm, Evolutionary algorithms, evolutionary computation, graphical technique, learning classifier, lightweight open source software, Low Quality Data, Probability distribution, Software algorithms, software tool, software tools, statistical analysis, statistical comparison},
	issn = {1098-7584},
	doi = {10.1109/FUZZ-IEEE.2013.6622418},
	author = {A. M. Palacios and L. S{\'a}nchez and I. Couso}
}
@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}
}
