@article {753,
title = {KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining.},
journal = {International Journal of Computational Intelligence Systems},
volume = {10},
number = {1},
year = {2017},
pages = {1238-1249},
issn = {1875-6891},
author = {I. Triguero and S. Gonzalez and J.M. Moyano and S. Garc{\'\i}a and J. Alcal{\'a}-Fdez and J. Luengo and A. Fern{\'a}ndez and M. J. del Jesus and L. S{\'a}nchez and F. Herrera and ATLANTIS PRESS.}
}
@conference {8015572,
title = {Mining association rules in R using the package RKEEL},
booktitle = {2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
year = {2017},
month = {July},
pages = {1-6},
abstract = {The discovery of fuzzy associations comprises a collection of data mining methods used to extract knowledge from large data sets. Although there is an extensive catalog of specialized algorithms that cover different aspects of the problem, the most recent approaches are not yet packaged in mainstream software environments. This makes it difficult to incorporate novel association rules methods to the data mining workflow. In this paper an extension of the RKEEL package is described that allows calling from the programming language R to those association rules methods contained in KEEL, which is one of the most comprehensive open source software suites. The potential of the proposed tool is illustrated through a case study comprising seven real-world datasets.},
keywords = {Computer science, data mining, data mining methods, data mining workflow, Electronic mail, fuzzy associations, fuzzy set theory, knowledge extraction, large data sets, Measurement, mining association rules, Open source software, open source software suites, programming language R, programming languages, public domain software, real-world datasets, RKEEL package, Software algorithms, software environments, software packages, Tools},
issn = {1558-4739},
doi = {10.1109/FUZZ-IEEE.2017.8015572},
author = {O. S{\'a}nchez and J. M. Moyano and L. S{\'a}nchez and J. Alc{\'a}la-F{\'a}dez}
}
@conference {7737717,
title = {Battery diagnosis for electrical vehicles through semi-physical fuzzy models},
booktitle = {2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
year = {2016},
month = {July},
pages = {416-423},
abstract = {An intelligent model of a Li-Ion battery for electrical vehicles is proposed that allows for a fast battery health evaluation without the need of removing the battery from the vehicle. The only data needed for performing the condition monitoring are logged performance records (currents and voltages) that are commonly available at Battery Management Systems. The model comprises a combination of differential equations and fuzzy rule-based systems, these last being embedded as non-linear blocks in the differential equations. Fuzzy rules are learnt from data with the help of metaheuristics and fine tuned with gradient descent algorithms. A TensorFlow{\texttrademark} implementation of the learning algorithm has been developed that takes advantage of the numerical processing capabilities of a massively parallel GPU and improves the learning speed by a high margin.},
keywords = {Batteries, battery diagnosis, battery health evaluation, battery management systems, Battery models, battery powered vehicles, Computational modeling, differential equations, Discharges (electric), electrical vehicles, fuzzy models, Fuzzy rule-based systems, Fuzzy systems, gradient descent algorithms, graphics processing units, learning (artificial intelligence), learning algorithm, Li-FePO4batteries, Li-ion battery, Liquids, massively parallel GPU, Mathematical model, nonlinear blocks, Numerical models, parallel processing, power engineering computing, secondary cells, semiphysical fuzzy models, TensorFlow, TensorFlow{\texttrademark}, Vehicles},
doi = {10.1109/FUZZ-IEEE.2016.7737717},
author = {L. S{\'a}nchez and J. Otero and I. Couso and C. Blanco}
}
@conference {7338079,
title = {A software tool to efficiently manage the energy consumption of HPC clusters},
booktitle = {2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
year = {2015},
month = {Aug},
pages = {1-8},
abstract = {Today, High Performance Computing clusters (HPC) are an essential tool owing to they are an excellent platform for solving a wide range of problems through parallel and distributed applications. Nonetheless, HPC clusters consume large amounts of energy, which combined with notably increasing electricity prices are having an important economical impact, forcing owners to reduce operation costs. In this work we propose a software, named EECluster, to reduce the high energy consumption of HPC clusters. EECluster works with both OGE/SGE and PBS/TORQUE resource management systems and automatically tunes its decision-making mechanism based on a machine learning approach. The quality of the obtained results using this software are evaluated by means of experiments made using actual workloads from the Scientific Modelling Cluster at Oviedo University and the academic-cluster used by the Oviedo University for teaching high performance computing subjects.},
keywords = {academic-cluster, Computational modeling, Computer architecture, Decision making, decision-making mechanism, EECluster software tool, Fuzzy systems, Genetics, high performance computing clusters, HPC cluster energy consumption, learning (artificial intelligence), machine learning approach, OGE-SGE resource management systems, Oviedo University, parallel processing, PBS-TORQUE resource management systems, power aware computing, resource allocation, Scientific Modelling Cluster, software tools},
doi = {10.1109/FUZZ-IEEE.2015.7338079},
author = {A. Coca{\~n}a-Fern{\'a}ndez and L. S{\'a}nchez and J. Ranilla}
}
@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 {6622420,
title = {Engine Health Monitoring for engine fleets using fuzzy radviz},
booktitle = {2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
year = {2013},
month = {July},
pages = {1-8},
abstract = {A new algorithm for assessment of Engine Health Monitoring (EHM) data in aircraft is proposed. The diagnostic tool quantifies step changes, shifts and trends in EHM data by means of a transformation that aggregates concurrent readings of EHM data into a single fuzzy state. A Genetic Fuzzy System is used to detect the occurance of a specific trend of interest in the sequence of states. The activation of the rules is represented in a 2D map by means of an extension of the Radviz visualization algorithm to fuzzy data.},
keywords = {2D map, aerospace engineering, aerospace engines, aircraft, Bandwidth, condition monitoring, data visualisation, diagnostic tool, EHM data, engine fleets, engine health monitoring, Engines, fault diagnosis, fuzzy data, fuzzy Radviz, fuzzy set theory, genetic algorithms, genetic fuzzy system, Genetic Fuzzy Systems, Low Quality Data, Maintenance engineering, Market research, mechanical engineering computing, Monitoring, Radviz visualization algorithm, rule activation, single fuzzy state, states sequence, Temperature measurement, Turbines},
issn = {1098-7584},
doi = {10.1109/FUZZ-IEEE.2013.6622420},
author = {A. Mart{\'\i}nez and L. S{\'a}nchez and I. Couso}
}
@article {simidat237,
title = {KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework},
journal = {Journal of Multiple-Valued Logic and Soft Computing},
volume = {17},
number = {2-3},
year = {2011},
pages = {255-287},
author = {J. Alcal{\'a}-Fdez and A. Fern{\'a}ndez and J. Luengo and J. Derrac and S. Garc{\'\i}a and L. S{\'a}nchez and F. Herrera}
}
@conference {6007647,
title = {Using the Adaboost algorithm for extracting fuzzy rules from low quality data: Some preliminary results},
booktitle = {2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)},
year = {2011},
month = {June},
pages = {1263-1270},
abstract = {When the Adaboost algorithm is used for extracting fuzzy rules from data, each rule is regarded as a weak learner, and knowledge bases as assimilated to ensembles. In this paper we propose an extension of this framework for obtaining fuzzy rule-based classifiers from imprecise data. In the new approach, the mentioned search of the best rule at each iteration is carried out by a genetic algorithm with a fuzzy fitness function. The instances will be assigned fuzzy weights, however each fuzzy rule will be associated to a crisp number of votes.},
keywords = {Adaboost algorithm, Boosting, data handling, Electronic mail, fuzzy fitness function, fuzzy rule extraction, fuzzy rule-based classifiers, fuzzy set theory, Fuzzy systems, fuzzy weights, genetic algorithm, genetic algorithms, Genetic Fuzzy Systems, knowledge based systems, knowledge bases, learning (artificial intelligence), Low Quality Data, Merging, Optimization, pattern classification, Training},
issn = {1098-7584},
doi = {10.1109/FUZZY.2011.6007647},
author = {A. M. Palacios and L. S{\'a}nchez and I. Couso}
}
@conference {5454152,
title = {Introducing a genetic fuzzy linguistic combination method for bagging fuzzy rule-based multiclassification systems},
booktitle = {2010 4th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)},
year = {2010},
month = {March},
pages = {75-80},
abstract = {Many different fuzzy aggregation operators have been successfully used to combine the outputs provided by the individual classifiers in a multiclassification system. However, up to our knowledge, the use of fuzzy combination methods composed of a fuzzy system is less extended. By using a fuzzy linguistic rule-based classification system as a combination method, the resulting classifier ensemble would show a hierarchical structure and the operation of the latter component would be transparent to the user. Moreover, for the specific case of fuzzy multiclassification systems, the new approach could also become a smart way to allow fuzzy classifiers to deal with high dimensional problems avoiding the curse of dimensionality. The present contribution establishes the first basis in this direction by introducing a genetic fuzzy system-based framework to build the fuzzy linguistic combination method for a bagging fuzzy multiclassification system.},
keywords = {Bagging, bagging fuzzy multiclassification system, Classification tree analysis, classifier ensemble, Computer science, Decision trees, fuzzy aggregation operators, fuzzy linguistic combination methods, fuzzy linguistic rule-based system, Fuzzy reasoning, fuzzy set theory, Fuzzy systems, genetic algorithms, genetic fuzzy-based system, Genetics, knowledge based systems, learning (artificial intelligence), machine learning, Neural networks, pattern classification},
doi = {10.1109/GEFS.2010.5454152},
author = {L. S{\'a}nchez and O. Cord{\'o}n and A. Quirin and K. Trawinski}
}
@conference {5584797,
title = {Preprocessing vague imbalanced datasets and its use in genetic fuzzy classifiers},
booktitle = {International Conference on Fuzzy Systems},
year = {2010},
month = {July},
pages = {1-8},
abstract = {When there is a substantial difference between the number of cases of the majority and minority classes, minimum error-based classification systems tend to overlook these last instances. This can be corrected either by preprocessing the dataset or by altering the objective function of the classifier. In this paper we analyze the first approach, in the context of genetic fuzzy systems (GFS), and in particular of those that can operate with imprecisely observed and low quality data. We will analyze the different preprocessing mechanisms of imbalanced datasets and will show the necessity of extending these for solving those problems where the data is both imprecise and im-balanced. In addition, we include a comprehensive description of a new algorithm, able to preprocess imprecise imbalanced datasets. Several real-world datasets are used to evaluate the proposal.},
keywords = {Classification algorithms, Context, data handling, Euclidean distance, fuzzy set theory, Fuzzy systems, genetic algorithms, genetic fuzzy classifier, genetic fuzzy system, Genetics, imbalanced dataset preprocessing, minimum error based classification system, Nearest neighbor searches, objective function, pattern classification, Pediatrics, Training},
issn = {1098-7584},
doi = {10.1109/FUZZY.2010.5584797},
author = {A. M. Palacios and L. S{\'a}nchez and I. Couso}
}
@article {simidat95,
title = {KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems},
journal = {Soft Computing},
volume = {13},
number = {3},
year = {2009},
pages = {307-318},
doi = {10.1007/s00500-008-0323-y},
author = {J. Alcal{\'a}-Fdez and L. S{\'a}nchez and S. Garc{\'\i}a and M. J. del Jesus and S. Ventura and J.M. Garrell and J. Otero and C. Romero and J. Bacardit and V. M. Rivas and J.C. Fern{\'a}ndez and F. Herrera}
}
@conference {4484560,
title = {A first study on bagging fuzzy rule-based classification systems with multicriteria genetic selection of the component classifiers},
booktitle = {2008 3rd International Workshop on Genetic and Evolving Systems},
year = {2008},
month = {March},
pages = {11-16},
keywords = {Bagging, bagging fuzzy rule-based classification system, Boosting, component classifier, Design methodology, evolutionary computation, fuzzy set theory, Fuzzy systems, genetic algorithms, heuristic fuzzy classification rule generation method, Humans, knowledge based systems, learning (artificial intelligence), machine learning, multicriteria genetic algorithm, pattern classification, Proposals, Scalability},
doi = {10.1109/GEFS.2008.4484560},
author = {O. Cord{\'o}n and A. Quirin and L. S{\'a}nchez}
}
@conference {simidat44,
title = {KEEL: A Data Mining Software Tool Integrating Genetic Fuzzy Systems},
booktitle = {3rd International Workshop on Genetic and Evolving Fuzzy Systems (GEFS)},
year = {2008},
note = {TIN-2008-06681-C06-01,TIN-2008-06681-C06-02,TIC-3928},
pages = {83-88},
address = {WittenBommerholz (Germany)},
author = {J. Alcal{\'a}-Fdez and S. Garc{\'\i}a and F. J. Berlanga and A. Fern{\'a}ndez and L. S{\'a}nchez and M. J. del Jesus and F. Herrera}
}
@conference {4626687,
title = {On the Use of Bagging, Mutual Information-Based Feature Selection and Multicriteria Genetic Algorithms to Design Fuzzy Rule-Based Classification Ensembles},
booktitle = {2008 Eighth International Conference on Hybrid Intelligent Systems},
year = {2008},
month = {Sep.},
pages = {549-554},
abstract = {In this contribution we explore the combination of bagging with random subspace and two variants of Battiti{\textquoteright}s mutual information feature selection methods to design fuzzy rule-based classification system ensembles. Besides, we consider a multicriteria genetic algorithm guided by the training error to select the component classifiers, in order to look for appropriate accuracy-complexity trade-offs in the final multiclassifier.},
keywords = {Bagging, classification, Classification algorithms, fuzzy rule-based classification ensembles, fuzzy set theory, Gallium, genetic algorithms, Glass, multicriteria genetic algorithms, mutual information-based feature selection, Sonar, Training, Vehicles},
doi = {10.1109/HIS.2008.147},
author = {O. Cord{\'o}n and A. Quirin and L. S{\'a}nchez}
}
@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 {4222979,
title = {Modeling Vague Data with Genetic Fuzzy Systems under a Combination of Crisp and Imprecise Criteria},
booktitle = {2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making},
year = {2007},
month = {April},
pages = {30-37},
abstract = {Multicriteria genetic algorithms can produce fuzzy models with a good balance between their precision and their complexity. The accuracy of a model is usually measured by the mean squared error of its residual. When vague training data is used, the residual becomes a fuzzy number, and it is needed to optimize a combination of crisp and fuzzy objectives in order to learn balanced models. In this paper, we will extend the NSGA-II algorithm to this last case, and test it over a practical problem of causal modeling in marketing. Different setups of this algorithm are compared, and it is shown that the algorithm proposed here is able to improve the generalization properties of those models obtained from the defuzzified training data.},
keywords = {Additive noise, combination, Computer science, crisp objectives, defuzzified training data, fuzzy logic, fuzzy models, fuzzy objectives, Fuzzy systems, generalisation (artificial intelligence), generalization, genetic algorithms, Genetic Fuzzy Systems, Global Positioning System, mean squared error, multicriteria genetic algorithms, Noise measurement, NSGA-II algorithm, Position measurement, Probability distribution, Stochastic resonance, Training data, vague data modeling},
doi = {10.1109/MCDM.2007.369413},
author = {L. S{\'a}nchez and I. Couso and J. Casillas}
}
@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}
}
@article {simidat14,
title = {Induction of Fuzzy Rule Based Classifiers with Evolutionary Boosting Algorithms},
journal = {IEEE Transactions on Fuzzy Systems},
volume = {12},
number = {3},
year = {2004},
pages = {296-308},
author = {M. J. del Jesus and F. Hoffmann and L. Junco 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}
}
@article {843495,
title = {Interval-valued GA-P algorithms},
journal = {IEEE Transactions on Evolutionary Computation},
volume = {4},
number = {1},
year = {2000},
month = {April},
pages = {64-72},
abstract = {When genetic programming (GP) methods are applied to solve symbolic regression problems, we obtain a point estimate of a variable, but it is not easy to calculate an associated confidence interval. We designed an interval arithmetic-based model that solves this problem. Our model extends a hybrid technique, the GA-P method, that combines genetic algorithms and genetic programming. Models based on interval GA-P can devise an interval model from examples and provide the algebraic expression that best approximates the data. The method is useful for generating a confidence interval for the output of a model, and also for obtaining a robust point estimate from data which we know to contain outliers. The algorithm was applied to a real problem related to electrical energy distribution. Classical methods were applied first, and then the interval GA-P. The results of both studies are used to compare interval GA-P with GP, GA-P, classical regression methods, neural networks, and fuzzy models.},
keywords = {Arithmetic, Computer science, confidence interval, electrical energy distribution, Fuzzy neural networks, Fuzzy systems, genetic algorithms, Genetic programming, Neural networks, point estimate, Robustness, statistical analysis, symbol manipulation, symbolic regression},
issn = {1089-778X},
doi = {10.1109/4235.843495},
author = {L. S{\'a}nchez}
}