@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 {4295638,
	title = {Niching genetic feature selection algorithms applied to the design of fuzzy rule-based classification systems},
	booktitle = {2007 IEEE International Fuzzy Systems Conference},
	year = {2007},
	month = {July},
	pages = {1-6},
	abstract = {In the design of fuzzy rule-based classification systems (FRBCSs) a feature selection process which determines the most relevant features is a crucial component in the majority of the classification problems. This simplification process increases the efficiency of the design process, improves the interpretability of the FRBCS obtained and its generalization capacity. Most of the feature selection algorithms provide a set of variables which are adequate for the induction process according to different quality measures. Nevertheless it can be useful for the induction process to determine not only a set of variables but also different set of variables. These sets of variables can be used for the design of a set of FRBCSs which can be combined in a multiclassifler system, improving the prediction capacity increasing its description capacity. In this work, different proposals of niching genetic algorithms for the feature selection process are analyzed. The different sets of features provided by them are used in a multiclassifier system designed by means of a genetic proposal. The experimentation shows the adaptation of this type of genetic algorithms to the FRBCS design.},
	keywords = {Algorithm design and analysis, classification, data mining, Databases, description capacity, Feature extraction, feature selection algorithms, Fuzzy reasoning, fuzzy rule-based classification systems, fuzzy set theory, Fuzzy sets, Fuzzy systems, genetic algorithms, induction process, Knowledge representation, multiclassifler system, niching genetic algorithms, prediction capacity, Process design, Proposals},
	issn = {1098-7584},
	doi = {10.1109/FUZZY.2007.4295638},
	author = {Jos{\'e} Aguilera and M. Chica and M. J. del Jesus and F. Herrera}
}
