@conference {10.1007/978-3-319-07617-1_30,
	title = {Mutual Information-Based Feature Selection in Fuzzy Databases Applied to Searching for the Best Code Metrics in Automatic Grading},
	booktitle = {Hybrid Artificial Intelligence Systems},
	year = {2014},
	pages = {330{\textendash}341},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Massive open online courses have a large impact in developing countries, helping to improve education in poor regions. However, instructors cannot review open-ended work from students as they do in smaller class settings. In the context of computer science courses where students{\textquoteright} code is reviewed, there are some methods that use code metrics for automatically providing the student with a qualification. Notwithstanding this, a high number of incomplete and conflicting sources of information must be combined in the prediction process, and it may happen that there are too many variables involved to make any meaningful predictions. In this work a new method is proposed for sorting a set of metrics by their relevance in the prediction of student qualifications that can cope with incomplete and imprecise results. Measurements taken on variable-sized sets of assignments are aggregated into fuzzy values, and a fuzzy random variable-based definition of mutual information is used to build a partial ranking of metrics according to their predictive power. The most relevant metrics are fed to a genetic fuzzy system that models the dependence between the fuzzy code metrics and the qualifications of the corresponding students. A set comprising 800 source code files, collected in classroom Computer Science lectures taught between 2013 and 2014, was used for validating the hypotheses of this research, and it was found that the new ranking method significantly improves the predictive capability of the models.},
	isbn = {978-3-319-07617-1},
	author = {Otero, Jos{\'e} and Su{\'a}rez, Rosario and S{\'a}nchez, Luciano},
	editor = {Polycarpou, Marios and de Carvalho, Andr{\'e} C. P. L. F. and Pan, Jeng-Shyang and Wo{\'z}niak, Micha{\l} and Quinti{\'a}n, H{\'e}ctor and Corchado, Emilio}
}
@conference {10.1007/978-3-319-08644-6_31,
	title = {Selecting the Most Informative Inputs in Modelling Problems with Vague Data Applied to the Search of Informative Code Metrics for Continuous Assessment in Computer Science Online Courses},
	booktitle = {Rough Sets and Current Trends in Computing},
	year = {2014},
	pages = {299{\textendash}308},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Sorting a set of inputs for relevance in modeling problems may be ambiguous if the data is vague. A general extension procedure is proposed in this paper that allows applying different deterministic or random feature selection algorithms to fuzzy data. This extension is based on a model of the relevance of a feature as a possibility distribution. The possibilistic relevances are ordered with the help of a fuzzy ranking. A practical problem where the most informative software metrics are searched for in an automatic grading problem is solved with this technique.},
	isbn = {978-3-319-08644-6},
	author = {Otero, Jos{\'e} and Del Rosario Su{\'a}rez, Maria and Palacios, Ana and Couso, In{\'e}s and S{\'a}nchez, Luciano},
	editor = {Cornelis, Chris and Kryszkiewicz, Marzena and {\'S}lȩzak, Dominik and Ruiz, Ernestina Menasalvas and Bello, Rafael and Shang, Lin}
}
@conference {inproceedings,
	title = {Managing stochastic algorithms cross-validation variability using an interval valued multiple comparison procedure},
	booktitle = {International Conference on Intelligent Systems Design and Applications},
	year = {2011},
	month = {11},
	pages = {1391-1396},
	doi = {10.1109/ISDA.2011.6121854},
	author = {Otero, Jos{\'e} and S{\'a}nchez, Luciano and Palacios, Ana and Couso, In{\'e}s}
}
@article {article,
	title = {Obtaining linguistic fuzzy rule-based regression models from imprecise data with multiobjective genetic algorithms},
	journal = {Soft Comput.},
	volume = {13},
	year = {2009},
	month = {03},
	pages = {467-479},
	doi = {10.1007/s00500-008-0362-4},
	author = {S{\'a}nchez, Luciano and Otero, Jos{\'e} and Couso, In{\'e}s}
}
@article {Villar2009,
	title = {Taximeter verification with GPS and soft computing techniques},
	journal = {Soft Computing},
	volume = {14},
	number = {4},
	year = {2009},
	month = {Mar},
	pages = {405},
	abstract = {Until recently, local governments in Spain were using machines with rolling cylinders for verifying taximeters. However, the condition of the tires can lead to errors in the process and the mechanical construction of the test equipment is not compatible with certain vehicles. Thus, a new measurement device needs to be designed. In our opinion, the verification of a taximeter will not be reliable unless measurements taken on an actual taxi run are used. GPS sensors are intuitively well suited for this process, because they provide the position and the speed with independence of those car devices that are under test. But there are legal problems that make difficult the use of GPS-based sensors: GPS coordinate measurements do not match exactly real coordinates and, generally speaking, we are not given absolute tolerances. We can not know whether the maximum error is always lower than, for example, 7~m. However, we might know that 50{\%} of the measurements lie on a circle with a radius of 7~m, centered on the real position. In this paper we describe a practical application where these legal problems have been solved with soft computing based technologies. In particular, we propose to characterize the uncertainty in the GPS with fuzzy techniques, so that we can reuse certain recent algorithms, formerly intended for being used in genetic fuzzy systems, to this new context. Specifically, we propose a new method for computing an upper bound of the length of the trajectory, taking into account the vagueness of the GPS data. This bound will be computed using a modified multiobjective evolutionary algorithm, which can optimize a fuzzy valued function. The accuracy of the measurements will be improved further by combining it with restrictions based on the dynamic behavior of the vehicles.},
	issn = {1433-7479},
	doi = {10.1007/s00500-009-0414-4},
	url = {https://doi.org/10.1007/s00500-009-0414-4},
	author = {Villar, Jos{\'e} and Otero, Adolfo and Otero, Jos{\'e} and S{\'a}nchez, Luciano}
}
@conference {inproceedings,
	title = {Selecting an Appropriate Statistical Test for Comparing Multiple Experiments in Evolutionary Machine Learning},
	year = {2007},
	month = {09},
	author = {Otero, Jos{\'e} and S{\'a}nchez, Luciano and Alcala-Fdez, Jesus}
}
@conference {Villar:2006:GAE:2176301.2176330,
	title = {Genetic Algorithms for Estimating Longest Path from Inherently Fuzzy Data Acquired with GPS},
	booktitle = {Proceedings of the 7th International Conference on Intelligent Data Engineering and Automated Learning},
	series = {IDEAL{\textquoteright}06},
	year = {2006},
	pages = {232{\textendash}240},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Berlin, Heidelberg},
	isbn = {3-540-45485-3, 978-3-540-45485-4},
	doi = {10.1007/11875581_28},
	url = {http://dx.doi.org/10.1007/11875581_28},
	author = {Villar, Jos{\'e} and Otero, Adolfo and Otero, Jos{\'e} and S{\'a}nchez, Luciano}
}
@article {Otero2006,
	title = {Induction of descriptive fuzzy classifiers with the Logitboost algorithm},
	journal = {Soft Computing},
	volume = {10},
	number = {9},
	year = {2006},
	month = {Jul},
	pages = {825{\textendash}835},
	abstract = {Recently, Adaboost has been compared to greedy backfitting of extended additive models in logistic regression problems, or {\textquoteleft}{\textquoteleft}Logitboost{\textquoteleft}{\textquoteleft}. The Adaboost algorithm has been applied to learn fuzzy rules in classification problems, and other backfitting algorithms to learn fuzzy rules in modeling problems but, up to our knowledge, there are not previous works that extend the Logitboost algorithm to learn fuzzy rules in classification problems.},
	issn = {1433-7479},
	doi = {10.1007/s00500-005-0011-0},
	url = {https://doi.org/10.1007/s00500-005-0011-0},
	author = {Otero, Jos{\'e} and S{\'a}nchez, Luciano}
}
@article {article,
	title = {A fast genetic method for inducting descriptive fuzzy models},
	journal = {Fuzzy Sets and Systems},
	volume = {141},
	year = {2004},
	month = {01},
	pages = {33-46},
	doi = {10.1016/S0165-0114(03)00112-X},
	author = {S{\'a}nchez, Luciano and Otero, Jos{\'e}}
}
@conference {article,
	title = {Preliminary results on the application of boosting to learn weighted fuzzy rules under single-winner inference},
	booktitle = {Congreso espa{\~n}ol de metaheur{\'\i}sticas, algoritmos evolutivos y bioinspirados.},
	year = {2004},
	address = {C{\'o}rdoba-Espa{\~n}a},
	author = {S{\'a}nchez, Luciano and Otero, Jos{\'e} and Su{\'a}rez, Mar{\'\i}a}
}
@inbook {S{\'a}nchez2003,
	title = {Tuning fuzzy partitions or assigning weights to fuzzy rules: which is better?},
	booktitle = {Accuracy Improvements in Linguistic Fuzzy Modeling},
	year = {2003},
	pages = {366{\textendash}385},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {The accuracy of linguistic classifiers can be improved with several techniques, but they all compromise the interpretability of the rule base up to a certain degree. Assigning weights to fuzzy rules and tuning the memberships associated to linguistic variables are two of the most common methods. In this work we study whether tuning the membership functions in a linguistic classifier is better or not than adjusting rule weights, in terms of the interpretability of the rule base and the complexity of the output.},
	isbn = {978-3-540-37058-1},
	doi = {10.1007/978-3-540-37058-1_15},
	url = {https://doi.org/10.1007/978-3-540-37058-1_15},
	author = {S{\'a}nchez, Luciano and Otero, Jos{\'e}},
	editor = {Casillas, Jorge and O. Cord{\'o}n and F. Herrera and Magdalena, Luis}
}
@conference {738,
	title = {Optimizaci{\'o}n gen{\'e}tico borrosa de par{\'a}metros en un sistema de bajo coste para la medici{\'o}n visual de cotas de veh{\'\i}culos},
	booktitle = {Congreso Espa{\~n}ol sobre tecnolog{\'\i}as y l{\'o}gica fuzzy},
	year = {2002},
	month = {01},
	address = {Le{\'o}n (Espa{\~n}a)},
	author = {Otero, Jos{\'e} and Otero, Adolfo and Junco, Luis and S{\'a}nchez, Luciano}
}
