@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 {1681710, title = {Knowledge Extraction from Fuzzy Data for Estimating Consumer Behavior Models}, booktitle = {2006 IEEE International Conference on Fuzzy Systems}, year = {2006}, month = {July}, pages = {164-170}, abstract = {For certain problems of casual modeling in marketing, the information is obtained by means of questionnaires. When these questionnaires include more than one item for each observable variable, the value of this variable can not be assigned a number, but a potentially scattered set of values. In this paper, we propose to represent the information contained in this set of values by means of a fuzzy number. A novel fuzzy statistics-based interpretation of the semantic of a fuzzy set will be used for this purpose, as we will consider that this fuzzy number is a nested family of confidence intervals for a central tendency measure of the value of the variable. A genetic learning algorithm, able to extract association fuzzy rules from this data, is also proposed. The accuracy of the model will be expressed by means of a fuzzy-valued function. We propose to jointly minimize this function and the complexity of the rule based model with multicriteria genetic algorithms, that in turn will depend on a fuzzy ranking-based ordering of individuals.}, keywords = {Artificial intelligence, association fuzzy rule extraction, casual modeling, Computer science, Computer science education, Consumer behavior, consumer behavior model, consumer behaviour, data mining, fuzzy data, fuzzy logic, fuzzy number, fuzzy ranking, fuzzy set theory, Fuzzy sets, fuzzy statistics, genetic algorithms, knowledge extraction, learning (artificial intelligence), learning algorithm, marketing data processing, multicriteria genetic algorithm, Scattering, semantic interpretation, statistical analysis}, issn = {1098-7584}, doi = {10.1109/FUZZY.2006.1681710}, author = {J. Casillas and L. S{\'a}nchez} } @conference {943781, title = {A fast genetic method for inducting linguistically understandable fuzzy models}, booktitle = {Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)}, volume = {3}, year = {2001}, month = {July}, pages = {1559-1563 vol.3}, abstract = {Fuzzy rule bases can be regarded as mixtures of experts, and boosting techniques can be applied to learn them from data. In particular, provided that adequate reasoning methods are used, fuzzy models are extended additive models, thus backfitting can be applied to them. We propose to use an implementation of backfitting that uses a genetic algorithm for fitting submodels to residuals and we also show that it is both more accurate and faster than other fuzzy rule learning methods.}, keywords = {Artificial intelligence, backfitting, boosting techniques, computational linguistics, extended additive models, fast genetic method, fuzzy logic, fuzzy models, Fuzzy reasoning, fuzzy rule bases, fuzzy rule learning methods, fuzzy set theory, Fuzzy sets, genetic algorithm, genetic algorithms, inference mechanisms, learning (artificial intelligence), Learning systems, linguistically understandable fuzzy model induction, mixtures of experts, reasoning methods, residuals, submodel fitting, Training data, uncertainty handling}, doi = {10.1109/NAFIPS.2001.943781}, author = {L. S{\'a}nchez} }