@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 {4295659,
	title = {Learning Fuzzy Linguistic Models from Low Quality Data by Genetic Algorithms},
	booktitle = {2007 IEEE International Fuzzy Systems Conference},
	year = {2007},
	month = {July},
	pages = {1-6},
	abstract = {Incremental rule base learning techniques can be used to learn models and classifiers from interval or fuzzy-valued data. These algorithms are efficient when the observation error is small. This paper is about datasets with medium to high discrepancies between the observed and the actual values of the variables, such as those containing missing values and coarsely discretized data. We will show that the quality of the iterative learning degrades in this kind of problems, and that it does not make full use of all the available information. As an alternative, we propose a new implementation of a mutiobjective Michigan-like algorithm, where each individual in the population codifies one rule and the individuals in the Pareto front form the knowledge base.},
	keywords = {Degradation, Fuzzy sets, Fuzzy systems, genetic algorithms, Global Positioning System, incremental rule base learning techniques, Iterative algorithms, iterative learning degrades, knowledge based systems, learning (artificial intelligence), learning fuzzy linguistic models, Low Quality Data, Noise measurement, Pareto front form, Pareto optimisation, Position measurement, Stochastic resonance, Uncertainty},
	issn = {1098-7584},
	doi = {10.1109/FUZZY.2007.4295659},
	author = {L. S{\'a}nchez and J. Otero}
}
@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}
}
@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}
}
@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}
}
@conference {943783,
	title = {Genetic tuning of fuzzy rule-based systems integrating linguistic hedges},
	booktitle = {Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)},
	volume = {3},
	year = {2001},
	month = {July},
	pages = {1570-1574 vol.3},
	abstract = {Tuning fuzzy rule-based systems for linguistic modeling is an interesting and widely developed task. It involves adjusting the membership functions composing the knowledge base. To do that, changing the parameters defining each membership function as using linguistic hedges to slightly modify them may be considered. This paper introduces a genetic tuning process for jointly making these two tuning approaches. The experimental results show that our method obtains accurate linguistic models in both approximation and generalization aspects.},
	keywords = {Computer science, experimental results, fuzzy logic, Fuzzy rule-based systems, Fuzzy sets, Fuzzy systems, generalisation (artificial intelligence), generalization, genetic algorithms, genetic tuning process, knowledge base, knowledge based systems, linguistic hedges, linguistic modeling, membership functions, Proposals, Shape, Takagi-Sugeno model, Timing, uncertainty handling},
	doi = {10.1109/NAFIPS.2001.943783},
	author = {J. Casillas and O. Cord{\'o}n and F. Herrera and M. J. del Jesus}
}
