@article {OTERO201642,
	title = {Finding informative code metrics under uncertainty for predicting the pass rate of online courses},
	journal = {Information Sciences},
	volume = {373},
	year = {2016},
	pages = {42 - 56},
	abstract = {A method is proposed for predicting the pass rate of a Computer Science course. Input data comprises different software metrics that are evaluated on a set of programs, comprising students{\textquoteright} answers to a list of computing challenges proposed by the course instructor. Different kinds of uncertainty are accepted, including missing answers and multiple responses to the same challenge. The most informative metrics are selected according to an extension to vague data of the observed Fisher information. The proposed method was tested on experimental data collected during two years at Oviedo University. Yearly changes in the pass rate of two groups were accurately predicted on the basis of 7 software metrics. 73 volunteer students and 1500 source files were involved in the experimentation.},
	keywords = {Automatic grading, feature selection, Genetic Fuzzy Systems, Low Quality Data, vague data},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/j.ins.2016.08.090},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025516306715},
	author = {Jos{\'e} Otero and Luis Junco and Rosario Su{\'a}rez and Ana Palacios and In{\'e}s Couso and Luciano S{\'a}nchez}
}
@article {PALACIOS201210212,
	title = {Eliciting a human understandable model of ice adhesion strength for rotor blade leading edge materials from uncertain experimental data},
	journal = {Expert Systems with Applications},
	volume = {39},
	number = {11},
	year = {2012},
	pages = {10212 - 10225},
	abstract = {The published ice adhesion performance data of novel {\textquotedblleft}ice-phobic{\textquotedblright} coatings varies significantly, and there are not reliable models of the properties of the different coatings that help the designer to choose the most appropriate material. In this paper it is proposed not to use analytical models but to learn instead a rule-based system from experimental data. The presented methodology increases the level of post-processing interpretation accuracy of experimental data obtained during the evaluation of ice-phobic materials for rotorcraft applications. Key to the success of this model is a possibilistic representation of the uncertainty in the data, combined with a fuzzy fitness-based genetic algorithm that is capable to elicit a suitable set of rules on the basis of incomplete and imprecise information.},
	keywords = {fuzzy rule-based classifiers, Genetic Fuzzy Systems, Ice-phobic materials, Isotropic materials, Shear adhesion strength, vague data},
	issn = {0957-4174},
	doi = {https://doi.org/10.1016/j.eswa.2012.02.155},
	url = {http://www.sciencedirect.com/science/article/pii/S0957417412004186},
	author = {Ana M. Palacios and Jos{\'e} L. Palacios and Luciano S{\'a}nchez}
}
@article {VILLAR2009250,
	title = {Taximeter verification using imprecise data from GPS},
	journal = {Engineering Applications of Artificial Intelligence},
	volume = {22},
	number = {2},
	year = {2009},
	pages = {250 - 260},
	abstract = {Until recently, local governments in Spain were using machines with rolling cylinders for testing and verification of taximeters. However, the tyres condition can lead to errors in the process and the mechanical construction of the test equipment is not compatible with certain vehicles. Thus, a new measurement device should be designed. In our opinion, the verification of a taximeter will not be reliable unless measurements taken on an actual taxi run are used. Global positioning system (GPS) sensors are intuitively well suited for this process, because they provide the position and the speed with independence from those car devices that are under test. Nevertheless, since GPS measurements are inherently imprecise, GPS-based sensors are difficult to homologate. In this paper we will show how these legal problems can be solved. We propose a method for computing an upper bound of the length of the trajectory, taking into account the vagueness of the GPS data. The uncertainty in the GPS data will be modelled by fuzzy techniques. The upper bound will be computed using a multiobjective evolutionary algorithm. The accuracy of the measurements will be improved further by combining it with restrictions based on the dynamic behavior of the vehicles.},
	keywords = {fuzzy fitness function, Genetic Fuzzy Systems, GPS, Metrology, vague data},
	issn = {0952-1976},
	doi = {https://doi.org/10.1016/j.engappai.2008.07.002},
	url = {http://www.sciencedirect.com/science/article/pii/S0952197608001267},
	author = {Jos{\'e} Villar and Adolfo Otero and Jos{\'e} Otero and Luciano S{\'a}nchez}
}
@article {SANCHEZ2008607,
	title = {Mutual information-based feature selection and partition design in fuzzy rule-based classifiers from vague data},
	journal = {International Journal of Approximate Reasoning},
	volume = {49},
	number = {3},
	year = {2008},
	pages = {607 - 622},
	abstract = {Algorithms for preprocessing databases with incomplete and imprecise data are seldom studied. For the most part, we lack numerical tools to quantify the mutual information between fuzzy random variables. Therefore, these algorithms (discretization, instance selection, feature selection, etc.) have to use crisp estimations of the interdependency between continuous variables, whose application to vague datasets is arguable. In particular, when we select features for being used in fuzzy rule-based classifiers, we often use a mutual information-based ranking of the relevance of inputs. But, either with crisp or fuzzy data, fuzzy rule-based systems route the input through a fuzzification interface. The fuzzification process may alter this ranking, as the partition of the input data does not need to be optimal. In our opinion, to discover the most important variables for a fuzzy rule-based system, we want to compute the mutual information between the fuzzified variables, and we should not assume that the ranking between the crisp variables is the best one. In this paper we address these problems, and propose an extended definition of the mutual information between two fuzzified continuous variables. We also introduce a numerical algorithm for estimating the mutual information from a sample of vague data. We will show that this estimation can be included in a feature selection algorithm, and also that, in combination with a genetic optimization, the same definition can be used to obtain the most informative fuzzy partition for the data. Both applications will be exemplified with the help of some benchmark problems.},
	keywords = {feature selection, Fuzzy fitness, Genetic Fuzzy Systems, vague data},
	issn = {0888-613X},
	doi = {https://doi.org/10.1016/j.ijar.2008.06.005},
	url = {http://www.sciencedirect.com/science/article/pii/S0888613X08001102},
	author = {Luciano S{\'a}nchez and M. Rosario Su{\'a}rez and J.R. Villar and In{\'e}s Couso}
}
@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}
}
@conference {4295665,
	title = {Some Results about Mutual Information-based Feature Selection and Fuzzy Discretization of Vague Data},
	booktitle = {2007 IEEE International Fuzzy Systems Conference},
	year = {2007},
	month = {July},
	pages = {1-6},
	abstract = {Algorithms for preprocessing databases with incomplete and imprecise data are seldom studied, partly because we lack numerical tools to quantify the interdependency between fuzzy random variables. In particular, many filter-type feature selection algorithms rely on crisp discretizations for estimating the mutual information between continuous variables, effectively preventing the use of vague data. Fuzzy rule based systems pass continuous input variables, in turn, through their own fuzzification interface. In the context of feature selection, should we rank the relevance of the inputs by means of their mutual information, it might happen that an apparently informative variable is useless after having been codified as a fuzzy subset of our catalog of linguistic terms. In this paper we propose to address both problems by estimating the mutual information with the same set of fuzzy partitions that will be used to codify the antecedents of the fuzzy rules. That is to say, we introduce a numerical algorithm for estimating the mutual information between two fuzzified continuous variables. This algorithm can be included in certain feature selection algorithms, and can also be used to obtain the most informative fuzzy partition for the data. The use of our definition will be exemplified with the help of some benchmark problems.},
	keywords = {codification, computational linguistics, Data preprocessing, Feature extraction, feature selection, fuzzification interface, fuzzy discretization, fuzzy random variables, fuzzy rule based systems, fuzzy set theory, Fuzzy sets, Fuzzy systems, Information filtering, Information filters, knowledge based systems, linguistic terms, mutual information, Partitioning algorithms, Random variables, Spatial databases, vague data},
	issn = {1098-7584},
	doi = {10.1109/FUZZY.2007.4295665},
	author = {L. S{\'a}nchez and M. R. Suarez and J. R. Villar and I. Couso}
}
@conference {4016720,
	title = {A Multiobjective Genetic Fuzzy System with Imprecise Probability Fitness for Vague Data},
	booktitle = {2006 International Symposium on Evolving Fuzzy Systems},
	year = {2006},
	month = {Sep.},
	pages = {131-136},
	abstract = {When questionnaires are designed, each factor under study can be assigned a set of different items. The answers to these questions must be merged in order to obtain the level of that input. Therefore, it is typical for data acquired from questionnaires that each of the inputs and outputs are not numbers, but sets of values. In this paper, we represent the information contained in such a set of values by means of a fuzzy number. A fuzzy statistics-based interpretation of the semantic of a fuzzy set is used for this purpose, as we consider that this fuzzy number is a nested family of confidence intervals for the value of the variable. The accuracy of the model is expressed by means of an interval-valued function, derived from a definition of the variance of a fuzzy random variable. A multicriteria genetic learning algorithm, able to optimize this interval-valued function, is proposed. As an example of the application of this algorithm, a practical problem of modeling in marketing is solved},
	keywords = {Computer errors, Computer science, Consumer behavior, fuzzy random variable variance, fuzzy set, fuzzy set theory, Fuzzy sets, fuzzy statistics-based interpretation, Fuzzy systems, genetic algorithms, Genetics, imprecise probability fitness, interval-valued function, learning (artificial intelligence), multicriteria genetic learning algorithm, multiobjective genetic fuzzy system, probability, Random variables, Statistics, vague data},
	doi = {10.1109/ISEFS.2006.251156},
	author = {L. S{\'a}nchez and I. Couso and J. Casillas}
}
