@article {COUSO201735,
	title = {Additive similarity and dissimilarity measures},
	journal = {Fuzzy Sets and Systems},
	volume = {322},
	year = {2017},
	note = {Theme: Preference and Similarity},
	pages = {35 - 53},
	abstract = {We develop a detailed formal study of the different variants of the notions of similarity and dissimilarity between fuzzy sets under the assumption of {\textquotedblleft}additivity{\textquotedblright}. A measure of (dis)similarity is said to be additive when it can be decomposed as the sum of the particular (dis)similarities of the compared memberships over the different elements of the universe. Some formal relations between existing formalizations of the notions of {\textquotedblleft}similarity{\textquotedblright} and {\textquotedblleft}dissimilarity{\textquotedblright} arise under this additional assumption. We conclude that, in this particular setting, one may basically distinguish between two main categories of comparison measures in the literature: those that depend on both the intersection and the difference of the compared items and those that only depend on their difference.},
	keywords = {Comparison measure, Dissimilarity measure, Divergence measure, Measure of resemblance, Measure of similitude, Similarity measure},
	issn = {0165-0114},
	doi = {https://doi.org/10.1016/j.fss.2016.12.013},
	url = {http://www.sciencedirect.com/science/article/pii/S0165011416304547},
	author = {In{\'e}s Couso and Luciano S{\'a}nchez}
}
@article {PALACIOS201660,
	title = {An extension of the FURIA classification algorithm to low quality data through fuzzy rankings and its application to the early diagnosis of dyslexia},
	journal = {Neurocomputing},
	volume = {176},
	year = {2016},
	note = {Recent Advancements in Hybrid Artificial Intelligence Systems and its Application to Real-World Problems},
	pages = {60 - 71},
	abstract = {An early detection and reeducation of dyslexic children is critical for their integration in the classroom. Parents and instructors can help the psychologist to detect potential cases of dyslexia before the children׳s writing age. Artificial intelligence tools can also assist in this task. Dyslexia symptoms are detected with tests whose results may be vague or ambiguous, thus machine learning techniques for low quality data are advised. In particular, in this paper it is suggested that a new extension to vague datasets of the classification algorithm FURIA (Fuzzy Unordered Rule Induction Algorithm) has advantages over other approaches in both the computational effort during the learning stage and the linguistic quality of the induced classification rules. The new approach is benchmarked with different test problems and compared to other artificial intelligence tools for dyslexia diagnosis in the literature.},
	keywords = {Dyslexia, FURIA, Fuzzy rule based classifiers, Low Quality Data},
	issn = {0925-2312},
	doi = {https://doi.org/10.1016/j.neucom.2014.11.088},
	url = {http://www.sciencedirect.com/science/article/pii/S0925231215005706},
	author = {Ana Palacios and Luciano S{\'a}nchez and In{\'e}s Couso and S{\'e}bastien Destercke}
}
@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 {COCANAFERNANDEZ201695,
	title = {Leveraging a predictive model of the workload for intelligent slot allocation schemes in energy-efficient HPC clusters},
	journal = {Engineering Applications of Artificial Intelligence},
	volume = {48},
	year = {2016},
	pages = {95 - 105},
	abstract = {A proactive mechanism to learn an efficient strategy for adaptive resource clusters is proposed. In contrast to reactive techniques, that rescale the cluster to fit the past load, a predictive strategy is adopted. The cluster incoming workload is forecasted and an optimization problem is defined whose solution is the optimal action according to a utility function. Genetic-based machine learning techniques are used, including multi-objective evolutionary algorithms under the distal supervised learning setup. Experimental evaluations show that the proactive system presented in this work improves either the energetic efficiency or the number of reconfigurations of previous approaches without a loss in the quality of service. Depending on the predictability of the workload, in real world cluster scenarios additional energy savings of up to approximately 40\% were measured over the best previous approach, with a 2{\texttimes} factor increment in the number of reconfigurations.},
	keywords = {Distal learning, Energy-efficient cluster computing, Evolutionary algorithms, Multi-criteria decision making},
	issn = {0952-1976},
	doi = {https://doi.org/10.1016/j.engappai.2015.10.003},
	url = {http://www.sciencedirect.com/science/article/pii/S0952197615002183},
	author = {Alberto Coca{\~n}a-Fern{\'a}ndez and Luciano S{\'a}nchez and Jos{\'e} Ranilla}
}
@article {COUSO2016129,
	title = {Machine learning models, epistemic set-valued data and generalized loss functions: An encompassing approach},
	journal = {Information Sciences},
	volume = {358-359},
	year = {2016},
	pages = {129 - 150},
	abstract = {We study those problems where the goal is to find {\textquotedblleft}optimal{\textquotedblright} models with respect to some specific criterion, in regression and supervised classification problems. Alternatives to the usual expected loss minimization criterion are proposed, and a general framework where this criterion can be seen as a particular instance of a general family of criteria is provided. In the new setting, each model is formally identified with a random variable that associates a loss value to each individual in the population. Based on this identification, different stochastic orderings between random variables lead to different criteria to compare pairs of models. Our general setting encompasses the classical criterion based on the minimization of the expected loss, but also other criteria where a numerical loss function is not available, and therefore the computation of its expectation does not make sense. The presentation of the new framework is divided into two stages. First, we consider the new framework under standard situations about the sample information, where both the collection of attributes and the response variables are observed with precision. Then, we assume that just incomplete information about them (expressed in terms of set-valued data sets) is provided. We cast some comparison criteria from the recent literature on learning methods from low-quality data as particular instances of our general approach.},
	keywords = {classification, Generalized stochastic ordering, Loss function, Low-quality data, Regression, Set-valued data},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/j.ins.2016.04.016},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025516302511},
	author = {In{\'e}s Couso and Luciano S{\'a}nchez}
}
@article {PALACIOS2015358,
	title = {Genetic learning of the membership functions for mining fuzzy association rules from low quality data},
	journal = {Information Sciences},
	volume = {295},
	year = {2015},
	pages = {358 - 378},
	abstract = {Many methods have been proposed to mine fuzzy association rules from databases with crisp values in order to help decision-makers make good decisions and tackle new types of problems. However, most real-world problems present a certain degree of imprecision. Various studies have been proposed to mine fuzzy association rules from imprecise data but they assume that the membership functions are known in advance and it is not an easy task to know a priori the most appropriate fuzzy sets to cover the domains of the variables. In this paper, we propose FARLAT-LQD, a new fuzzy data-mining algorithm to obtain both suitable membership functions and useful fuzzy association rules from databases with a wide range of types of uncertain data. To accomplish this, first we perform a genetic learning of the membership functions based on the 3-tuples linguistic representation model to reduce the search space and to learn the most adequate context for each fuzzy partition, maximizing the fuzzy supports and the interpretability measure GM3M in order to preserve the semantic interpretability of the obtained membership functions. Moreover, we propose a new algorithm based on the Fuzzy Frequent Pattern-growth algorithm, called FFP-growth-LQD, to efficiently mine the fuzzy association rules from inaccurate data considering the learned membership functions in the genetic process. The results obtained over 3 databases of different sizes and kinds of imprecisions demonstrate the effectiveness of the proposed algorithm.},
	keywords = {3-tuples linguistic representation, data mining, Fuzzy association rules, Genetic Fuzzy Systems, GM3M measure, Imprecise data},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/j.ins.2014.10.027},
	url = {http://www.sciencedirect.com/science/article/pii/S002002551401010X},
	author = {Ana Mar{\'\i}a Palacios and Jos{\'e} Luis Palacios and Luciano S{\'a}nchez and Jes{\'u}s Alcal{\'a}-Fdez}
}
@article {OTERO201544,
	title = {Local iterative DLT soft-computing vs. interval-valued stereo calibration and triangulation with uncertainty bounding in 3D reconstruction},
	journal = {Neurocomputing},
	volume = {167},
	year = {2015},
	pages = {44 - 51},
	abstract = {The use of stereo vision for 3D data gathering is affected by constraints in the position of the cameras, the quality of the optical elements and the numerical algorithms for calibration and matching. Also, there is not a wide agreement on the best procedure for bounding the 3D errors within an uncertainty volume. In this work, this problem is solved by implementing the whole set of computations, including calibration and triangulation, with interval data. This is in contrast with previous works that rely on Direct Linear Transform (DLT) as a camera model. To keep better with real lens aberrations, a local iterative modification is proposed that provides an on-demand set of calibration parameters for each 3D point, comprising those nearest in 3D space. In this way, the estimated camera parameters are closely related with camera aberrations at the lens area through which that 3D point is imaged. To further reduce the triangulation uncertainty volume, a Soft Computing approach is proposed that represents each 3D point uncertainty as a cloud of crisp points compatible with interval-valued calibration data. Real data from previous works in related research areas is used to judge whether the new approach improves the precision and accuracy of other crisp and interval-valued estimations without degrading precision, and it is concluded that the new technique is able to significantly improve the uncertainty volumes.},
	keywords = {Camera calibration, Computer vision, Interval-valued, Possibility, Stereo},
	issn = {0925-2312},
	doi = {https://doi.org/10.1016/j.neucom.2014.11.087},
	url = {http://www.sciencedirect.com/science/article/pii/S0925231215005512},
	author = {Jos{\'e} Otero and Luciano S{\'a}nchez}
}
@article {PALACIOS201510,
	title = {Sequential pattern mining applied to aeroengine condition monitoring with uncertain health data},
	journal = {Engineering Applications of Artificial Intelligence},
	volume = {44},
	year = {2015},
	pages = {10 - 24},
	abstract = {Numerical algorithms that can assess Engine Health Monitoring (EHM) data in aeroengines are influenced by the high level of uncertainty inherent to gas path measurements and engine-to-engine variability. Among them, fuzzy rule-based techniques have been successfully used due to their robustness towards noisy signals and their capability to learn human-readable rules from data. These techniques are useful in detecting the presence of certain types of abnormal events or general engine deterioration, through the identification of specific combinations of EHM signals associated with these specific cases. However, there are also other types of engine events that manifest themselves as an ordered sequence of otherwise normal combinations of the EHM signals. These combinations are dismissed when considered in isolation as the current existing techniques cannot assess them. In this paper it is proposed to use sequence mining techniques in order to obtain fuzzy rules from uncertain EHM data which can in turn be used to identify the cases where an engine event is determined as a sequence of otherwise normal combinations of EHM signals. The results are subsequently tested on a representative sample of aeroengine data.},
	keywords = {engine health monitoring, Fuzzy rule-based systems, Fuzzy sequence mining, Uncertain data},
	issn = {0952-1976},
	doi = {https://doi.org/10.1016/j.engappai.2015.05.003},
	url = {http://www.sciencedirect.com/science/article/pii/S0952197615001062},
	author = {Ana Palacios and Alvaro Mart{\'\i}nez and Luciano S{\'a}nchez and In{\'e}s Couso}
}
@article {COUSO2015127,
	title = {The behavioral meaning of the median},
	journal = {Information Sciences},
	volume = {294},
	year = {2015},
	note = {Innovative Applications of Artificial Neural Networks in Engineering},
	pages = {127 - 138},
	abstract = {We extend the notion of statistical preference to the general framework of imprecise probabilities, by proposing a new notion of desirability of gambles called {\textquotedblleft}sign-desirability{\textquotedblright}, different from the usual desirability notion in Walley{\textquoteright}s framework. We axiomatically characterize coherent families of sign-desirable gambles. We furthermore prove that the pair of lower and upper previsions of a gamble, according to this new desirability notion, coincides with the pair of bounds (infimum and supremum) of the set of medians associated to a coherent family of linear previsions. Thus, a general notion of median is naturally derived, and provided with a behavioral meaning. As a consequence of these results, the connection between statistical preference in classical Probability Theory, and the sign of the median of the difference of two random variables is laid bare.},
	keywords = {Desirable gamble, Imprecise Probability, Median, Statistical preference, Stochastic ordering},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/j.ins.2014.08.049},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025514008548},
	author = {In{\'e}s Couso and Seraf{\'\i}n Moral and Luciano S{\'a}nchez}
}
@article {SANCHEZ2014269,
	title = {A design methodology for semi-physical fuzzy models applied to the dynamic characterization of LiFePO4 batteries},
	journal = {Applied Soft Computing},
	volume = {14},
	year = {2014},
	note = {Evolving Soft Computing Techniques and Applications},
	pages = {269 - 288},
	abstract = {A methodology for designing semi-physical fuzzy models is proposed. Prior physical knowledge about the dynamics of the system is modeled with continuous time differential equations. Fuzzy knowledge bases are embedded in these equations as nonlinear constructive blocks. Rules comprising the knowledge bases are fitted to interval-valued data with metaheuristics. A possibilistic filter is proposed that is able to gradually evolve an initial estimation of the latent variables of the model on the basis of successive prediction errors. This methodology has been applied to the prediction of voltage and state of charge of LiFePO4 batteries. An empirical study has been carried over data gathered in experiments at the Battery Laboratory at Oviedo University. Fitting between the proposed model and actual measurements is studied for four different manufacturers and different charge{\textendash}discharge patterns. Predictions of the evolution of the voltage during charge, discharge and inactivity compare favorably to different models in the literature. The possibilistic filter allows to estimate the state of charge of batteries after an arbitrary path that may include partial charges and discharges. It is shown that the accuracy of the open loop model improves that of other approaches in the literature, and at the same time the observer-based online model is able to approximate the effective remnant charge of the battery after a reasonably short time.},
	keywords = {fuzzy models, Imprecise data, LiFePO batteries, Online identification, Semi-physical modeling},
	issn = {1568-4946},
	doi = {https://doi.org/10.1016/j.asoc.2013.03.020},
	url = {http://www.sciencedirect.com/science/article/pii/S156849461300135X},
	author = {Luciano S{\'a}nchez and In{\'e}s Couso and Manuela Gonz{\'a}lez}
}
@conference {Otero2014APF,
	title = {A Procedure for Extending Input Selection Algorithms to Low Quality Data in Modelling Problems with Application to the Automatic Grading of Uploaded Assignments},
	booktitle = {TheScientificWorldJournal},
	year = {2014},
	author = {Jos{\'e} Varela Otero and Ana M. Palacios and Rosario Su{\'a}rez and Luis Junco and In{\'e}s Couso and Luciano S{\'a}nchez}
}
@article {OTERO201488,
	title = {Bootstrap analysis of multiple repetitions of experiments using an interval-valued multiple comparison procedure},
	journal = {Journal of Computer and System Sciences},
	volume = {80},
	number = {1},
	year = {2014},
	pages = {88 - 100},
	abstract = {A new bootstrap test is introduced that allows for assessing the significance of the differences between stochastic algorithms in a cross-validation with repeated folds experimental setup. Intervals are used for modeling the variability of the data that can be attributed to the repetition of learning and testing stages over the same folds in cross validation. Numerical experiments are provided that support the following three claims: (1) Bootstrap tests can be more powerful than ANOVA or Friedman test for comparing multiple classifiers. (2) In the presence of outliers, interval-valued bootstrap tests achieve a better discrimination between stochastic algorithms than nonparametric tests. (3) Choosing ANOVA, Friedman or Bootstrap can produce different conclusions in experiments involving actual data from machine learning tasks.},
	keywords = {Cross validation, Statistical comparisons of algorithms, Tests for interval-valued data},
	issn = {0022-0000},
	doi = {https://doi.org/10.1016/j.jcss.2013.03.009},
	url = {http://www.sciencedirect.com/science/article/pii/S0022000013000731},
	author = {Jos{\'e} Otero and Luciano S{\'a}nchez and In{\'e}s Couso and Ana Palacios}
}
@article {SANCHEZ20141583,
	title = {Comments on {\textquotedblleft}Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization{\textquotedblright} by Eyke H{\"u}llermeier},
	journal = {International Journal of Approximate Reasoning},
	volume = {55},
	number = {7},
	year = {2014},
	note = {Special issue: Harnessing the information contained in low-quality data sources},
	pages = {1583 - 1587},
	abstract = {The paper by Eyke H{\"u}llermeier introduces a new set of techniques for learning models from imprecise data. The removal of the uncertainty in the training instances through the input{\textendash}output relationship described by the model is also considered. This discussion addresses three points of the paper: extension principle-based models, precedence operators between fuzzy losses and possible connections between data disambiguation and data imputation.},
	keywords = {classification, fuzzy data, Imprecise data, Loss functions, machine learning, Regression},
	issn = {0888-613X},
	doi = {https://doi.org/10.1016/j.ijar.2014.04.008},
	url = {http://www.sciencedirect.com/science/article/pii/S0888613X14000607},
	author = {Luciano S{\'a}nchez}
}
@article {Couso2014HarnessingTI,
	title = {Harnessing the information contained in low-quality data sources},
	journal = {Int. J. Approx. Reasoning},
	volume = {55},
	year = {2014},
	pages = {1485-1486},
	author = {In{\'e}s Couso and Luciano S{\'a}nchez}
}
@article {COUSO2014114,
	title = {The notion of roughness of a fuzzy set},
	journal = {Fuzzy Sets and Systems},
	volume = {249},
	year = {2014},
	note = {Theme: Automata Theory and Algebra},
	pages = {114 - 127},
	abstract = {The roughness of a set (according to the notion introduced by Pawlak in 1991) can be regarded as the MZ-distance between its upper and the lower approximations. With this idea in mind, we have generalized Pawlak{\textquoteright}s definition, by replacing the MZ-distance by a general {\textquotedblleft}distance{\textquotedblright} measure. We also generalize the notion of roughness of fuzzy sets introduced by Huyhn and Nakamori in 2005.},
	keywords = {Dissimilarity measure, Divergence measure, fuzzy set, Rough set, Roughness},
	issn = {0165-0114},
	doi = {https://doi.org/10.1016/j.fss.2014.02.013},
	url = {http://www.sciencedirect.com/science/article/pii/S0165011414000657},
	author = {In{\'e}s Couso and Laura Garrido and Luciano S{\'a}nchez}
}
@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}
}
@article {Couso2013SimilarityAD,
	title = {Similarity and dissimilarity measures between fuzzy sets: A formal relational study},
	journal = {Inf. Sci.},
	volume = {229},
	year = {2013},
	pages = {122-141},
	author = {In{\'e}s Couso and Laura Garrido 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 {COUSO201168,
	title = {Inner and outer fuzzy approximations of confidence intervals},
	journal = {Fuzzy Sets and Systems},
	volume = {184},
	number = {1},
	year = {2011},
	note = {Preference Modelling and Decision Analysis (Selected Papers from EUROFUSE 2009)},
	pages = {68 - 83},
	abstract = {We extend the notion of confidence region to fuzzy data, by defining a pair of fuzzy inner and outer confidence regions. We show the connection with previous proposals, as well as with recent studies on hypothesis testing with low quality data.},
	keywords = {Confidence region, Hypothesis testing, Possibility measure},
	issn = {0165-0114},
	doi = {https://doi.org/10.1016/j.fss.2010.11.004},
	url = {http://www.sciencedirect.com/science/article/pii/S0165011410004550},
	author = {In{\'e}s Couso and Luciano S{\'a}nchez}
}
@article {PALACIOS2011841,
	title = {Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data},
	journal = {International Journal of Approximate Reasoning},
	volume = {52},
	number = {6},
	year = {2011},
	pages = {841 - 862},
	abstract = {Cost-sensitive classification is based on a set of weights defining the expected cost of misclassifying an object. In this paper, a Genetic Fuzzy Classifier, which is able to extract fuzzy rules from interval or fuzzy valued data, is extended to this type of classification. This extension consists in enclosing the estimation of the expected misclassification risk of a classifier, when assessed on low quality data, in an interval or a fuzzy number. A cooperative-competitive genetic algorithm searches for the knowledge base whose fitness is primal with respect to a precedence relation between the values of this interval or fuzzy valued risk. In addition to this, the numerical estimation of this risk depends on the entrywise product of cost and confusion matrices. These have been, in turn, generalized to vague data. The flexible assignment of values to the cost function is also tackled, owing to the fact that the use of linguistic terms in the definition of the misclassification cost is allowed.},
	keywords = {Cost sensitive classification, genetic fuzzy system, Low Quality Data},
	issn = {0888-613X},
	doi = {https://doi.org/10.1016/j.ijar.2011.02.007},
	url = {http://www.sciencedirect.com/science/article/pii/S0888613X11000545},
	author = {Ana M. Palacios and Luciano S{\'a}nchez and In{\'e}s Couso}
}
@article {COUSO2011240,
	title = {Mark-recapture techniques in statistical tests for imprecise data},
	journal = {International Journal of Approximate Reasoning},
	volume = {52},
	number = {2},
	year = {2011},
	note = {Philosophy of Probability},
	pages = {240 - 260},
	abstract = {We aim to construct suitable tests when we have imprecise information about a sample. More specifically, we assume that we get a collection of n sets of values, each one characterizing an imprecise measurement. Each set specifies where the true sample value is (and where it is not) with full confidence, but it does not provide any additional information. Our main objectives are twofold: first we will review different kinds of tests in the literature about inferential statistics with random sets and discuss the approach that best suits our definition of imprecise data. Secondly, we will show that we can take advantage from mark and recapture techniques to improve the accuracy of our decisions. These techniques will be specially important when the population is small enough (with respect to the sample size) that recaptures are common. They also seem to be useful when resampling techniques are involved in the decision process.},
	keywords = {Hypothesis testing, Imprecise data analysis, Multi-valued mappings, Multi-valued test function, Probability bounds},
	issn = {0888-613X},
	doi = {https://doi.org/10.1016/j.ijar.2010.07.009},
	url = {http://www.sciencedirect.com/science/article/pii/S0888613X10000952},
	author = {In{\'e}s Couso and Luciano S{\'a}nchez}
}
@conference {Snchez2010AssessingTE,
	title = {Assessing the evolution of learning capabilities and disorders with graphical exploratory analysis of surveys containing missing and conflicting answers},
	year = {2010},
	author = {Luciano S{\'a}nchez and In{\'e}s Couso and Jos{\'e} Varela Otero and Ana M. Palacios}
}
@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 {COUSO2008237,
	title = {Higher order models for fuzzy random variables},
	journal = {Fuzzy Sets and Systems},
	volume = {159},
	number = {3},
	year = {2008},
	note = {Theme: Probability and Statistics},
	pages = {237 - 258},
	abstract = {A fuzzy random variable is viewed as the imprecise observation of the outcomes in a random experiment. Since randomness and vagueness coexist in the same framework, it seems reasonable to integrate fuzzy random variables into imprecise probabilities theory. Nevertheless, fuzzy random variables are commonly presented in the literature as classical measurable functions associated to a classical probability measure. We present here a higher order possibility model that represents the imprecise information provided by a fuzzy random variable. We compare it with previous classical models in the literature. First, some aspects about the acceptability function associated to a fuzzy random variable are investigated. Secondly, we present three different higher order possibility models, all of them arising in a natural way. We investigate their similarities and differences, and observe that the first one (the fuzzy probability envelope) is the most informative. Finally we compare the fuzzy probability envelope with the (classical) probability measure induced by the fuzzy random variable. We conclude that the classical probability measure does not always contain all relevant information provided by a fuzzy random variable.},
	keywords = {Fuzzy random variable, Imprecise probabilities, Random set, Second order possibility measure},
	issn = {0165-0114},
	doi = {https://doi.org/10.1016/j.fss.2007.09.004},
	url = {http://www.sciencedirect.com/science/article/pii/S0165011407003971},
	author = {In{\'e}s Couso 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 {SANCHEZ2008952,
	title = {Obtaining transparent models of chaotic systems with multi-objective simulated annealing algorithms},
	journal = {Information Sciences},
	volume = {178},
	number = {4},
	year = {2008},
	pages = {952 - 970},
	abstract = {Transparent models search for a balance between interpretability and accuracy. This paper is about the estimation of transparent models of chaotic systems from data, which are accurate and simple enough for their expression to be understandable by a human expert. The models we propose are discrete, built upon common blocks in control engineering (gain, delay, sum, etc.) and optimized both in their complexity and accuracy. The accuracy of a discrete model can be measured by means of the average error between its prediction for the next sampling period and the true output at that time, or {\textquoteleft}one-step error{\textquoteright}. A perfect model has zero one-step error, but a small error is not always associated with an approximate model, especially in chaotic systems. In chaos, an arbitrarily low difference between two initial states will produce uncorrelated trajectories, thus a model with a low one-step error may be very different from the desired one. Even though a recursive evaluation (multi-step prediction) improves the fitting, in this work we will show that a learning algorithm may not converge to an appropriate model, unless we include some terms that depend on estimates of certain properties of the model (so called {\textquoteleft}invariants{\textquoteright} of the chaotic series). We will show this graphically, by means of the reconstructed attractors of the original system and the model. Therefore, we also propose to follow a multi-objective approach to model chaotic processes and to apply a simulated annealing-based optimization to obtain transparent models.},
	keywords = {Chaotic systems, Genetic programming, Multi-objective simulated annealing, Transparent models},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/j.ins.2007.09.029},
	url = {http://www.sciencedirect.com/science/article/pii/S002002550700463X},
	author = {Luciano S{\'a}nchez and Jos{\'e} R. Villar}
}
@article {Snchez2007BoostingFR,
	title = {Boosting fuzzy rules in classification problems under single-winner inference},
	journal = {Int. J. Intell. Syst.},
	volume = {22},
	year = {2007},
	pages = {1021-1034},
	author = {Luciano S{\'a}nchez and Jos{\'e} Otero}
}
@conference {CanoCHS02,
	title = {A GRASP Algorithm for Clustering},
	booktitle = {Proceedings of the 8th Ibero-American Conference on Artifical Intelligence, Seville, Spain, November 12-15, 2002,},
	year = {2002},
	pages = {214{\textendash}223},
	author = {J. R. Cano and O. Cord{\'o}n and F. Herrera and Luciano S{\'a}nchez}
}
@article {CanoCHS02,
	title = {A greedy randomized adaptive search procedure applied to the clustering problem as an initialization process using K-Means as a local search procedure},
	journal = {Journal of Intelligent and Fuzzy Systems},
	volume = {12},
	number = {3-4},
	year = {2002},
	pages = {235{\textendash}242},
	author = {J. R. Cano and O. Cord{\'o}n and F. Herrera and Luciano S{\'a}nchez}
}
@article {SANCHEZ2002175,
	title = {Some relationships between fuzzy and random set-based classifiers and models},
	journal = {International Journal of Approximate Reasoning},
	volume = {29},
	number = {2},
	year = {2002},
	pages = {175 - 213},
	abstract = {When designing rule-based models and classifiers, some precision is sacrificed to obtain linguistic interpretability. Understandable models are not expected to outperform black boxes, but usually fuzzy learning algorithms are statistically validated by contrasting them with black-box models. Unless performance of both approaches is equivalent, it is difficult to judge whether the fuzzy one is doing its best, because the precision gap between the best understandable model and the best black-box model is not known. In this paper we discuss how to generate probabilistic rule-based models and classifiers with the same structure as fuzzy rule-based ones. Fuzzy models, in which features are partitioned into linguistic terms, will be compared to probabilistic rule-based models with the same number of terms in every linguistic partition. We propose to use these probabilistic models to estimate a lower precision limit which fuzzy rule learning algorithms should surpass.},
	keywords = {Fuzzy classifiers, fuzzy models, Random set-based classifiers, Random set-based models},
	issn = {0888-613X},
	doi = {https://doi.org/10.1016/S0888-613X(01)00063-9},
	url = {http://www.sciencedirect.com/science/article/pii/S0888613X01000639},
	author = {Luciano S{\'a}nchez and Jorge Casillas and O. Cord{\'o}n and M. J. del Jesus}
}
@article {SANCHEZ2001175,
	title = {Combining GP operators with SA search to evolve fuzzy rule based classifiers},
	journal = {Information Sciences},
	volume = {136},
	number = {1},
	year = {2001},
	note = {Recent Advances in Genetic Fuzzy Systems},
	pages = {175 - 191},
	abstract = {The genotype{\textendash}phenotype encoding of fuzzy rule bases in GA, along with their corresponding crossover and mutation operators, can be used by other search schemes, improving the behavior of these last ones. As a practical consequence of this, a simulated annealing-based method for inducting both parameters and structure of a fuzzy classifier has been developed. The adjacency operator in SA has been replaced with a macromutation taken from tree-shaped genotype GAs. We will show that results of SA search are similar to those of GP in both the efficiency of the learned classifiers and in its linguistic interpretability, while the memory consumption of the learning process is lower.},
	keywords = {Fuzzy classification, genetic algorithms, Genetic programming, Simulated annealing},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/S0020-0255(01)00146-3},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025501001463},
	author = {Luciano S{\'a}nchez and In{\'e}s Couso and J.A. Corrales}
}
@article {SANCHEZ1998343,
	title = {A random sets-based method for identifying fuzzy models},
	journal = {Fuzzy Sets and Systems},
	volume = {98},
	number = {3},
	year = {1998},
	pages = {343 - 354},
	abstract = {The nature of interference sources in signal processing is a key problem in many applied disciplines. These interferences are often modelled by random processes, although it has been shown that many models can be favourably modified when some of the uncertainty sources are treated as fuzzy experiments. Following this spirit, the objective of this paper is to build a mathematical model which explains a set of imprecise measurements taken on a physical system. Furthermore, it is assumed that the effect of unmodelled inputs to the system can be regarded as a random process, but the imprecision in the measures is better described by means of a possibility distribution.},
	keywords = {Control theory, Description length, Linguistic modelling, Random set},
	issn = {0165-0114},
	doi = {https://doi.org/10.1016/S0165-0114(97)00010-9},
	url = {http://www.sciencedirect.com/science/article/pii/S0165011497000109},
	author = {Luciano S{\'a}nchez}
}
