@article {PALACIOS2015358,
	title = {Genetic learning of the membership functions for mining fuzzy association rules from low quality data},
	journal = {Information Sciences},
	volume = {295},
	year = {2015},
	pages = {358 - 378},
	abstract = {Many methods have been proposed to mine fuzzy association rules from databases with crisp values in order to help decision-makers make good decisions and tackle new types of problems. However, most real-world problems present a certain degree of imprecision. Various studies have been proposed to mine fuzzy association rules from imprecise data but they assume that the membership functions are known in advance and it is not an easy task to know a priori the most appropriate fuzzy sets to cover the domains of the variables. In this paper, we propose FARLAT-LQD, a new fuzzy data-mining algorithm to obtain both suitable membership functions and useful fuzzy association rules from databases with a wide range of types of uncertain data. To accomplish this, first we perform a genetic learning of the membership functions based on the 3-tuples linguistic representation model to reduce the search space and to learn the most adequate context for each fuzzy partition, maximizing the fuzzy supports and the interpretability measure GM3M in order to preserve the semantic interpretability of the obtained membership functions. Moreover, we propose a new algorithm based on the Fuzzy Frequent Pattern-growth algorithm, called FFP-growth-LQD, to efficiently mine the fuzzy association rules from inaccurate data considering the learned membership functions in the genetic process. The results obtained over 3 databases of different sizes and kinds of imprecisions demonstrate the effectiveness of the proposed algorithm.},
	keywords = {3-tuples linguistic representation, data mining, Fuzzy association rules, Genetic Fuzzy Systems, GM3M measure, Imprecise data},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/j.ins.2014.10.027},
	url = {http://www.sciencedirect.com/science/article/pii/S002002551401010X},
	author = {Ana Mar{\'\i}a Palacios and Jos{\'e} Luis Palacios and Luciano S{\'a}nchez and Jes{\'u}s Alcal{\'a}-Fdez}
}
@article {SANCHEZ2014269,
	title = {A design methodology for semi-physical fuzzy models applied to the dynamic characterization of LiFePO4 batteries},
	journal = {Applied Soft Computing},
	volume = {14},
	year = {2014},
	note = {Evolving Soft Computing Techniques and Applications},
	pages = {269 - 288},
	abstract = {A methodology for designing semi-physical fuzzy models is proposed. Prior physical knowledge about the dynamics of the system is modeled with continuous time differential equations. Fuzzy knowledge bases are embedded in these equations as nonlinear constructive blocks. Rules comprising the knowledge bases are fitted to interval-valued data with metaheuristics. A possibilistic filter is proposed that is able to gradually evolve an initial estimation of the latent variables of the model on the basis of successive prediction errors. This methodology has been applied to the prediction of voltage and state of charge of LiFePO4 batteries. An empirical study has been carried over data gathered in experiments at the Battery Laboratory at Oviedo University. Fitting between the proposed model and actual measurements is studied for four different manufacturers and different charge{\textendash}discharge patterns. Predictions of the evolution of the voltage during charge, discharge and inactivity compare favorably to different models in the literature. The possibilistic filter allows to estimate the state of charge of batteries after an arbitrary path that may include partial charges and discharges. It is shown that the accuracy of the open loop model improves that of other approaches in the literature, and at the same time the observer-based online model is able to approximate the effective remnant charge of the battery after a reasonably short time.},
	keywords = {fuzzy models, Imprecise data, LiFePO batteries, Online identification, Semi-physical modeling},
	issn = {1568-4946},
	doi = {https://doi.org/10.1016/j.asoc.2013.03.020},
	url = {http://www.sciencedirect.com/science/article/pii/S156849461300135X},
	author = {Luciano S{\'a}nchez and In{\'e}s Couso and Manuela Gonz{\'a}lez}
}
@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}
}
