@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}
}
@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 {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}
}
