@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 {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 {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}
}
@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}
}
