@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}
}
@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}
}
@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 {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}
}