@conference {Charte:HAIS21,
	title = {Slicer: Feature Learning for Class Separability with Least-Squares Support Vector Machine Loss and COVID-19 Chest X-Ray Case Study},
	booktitle = {Hybrid Artificial Intelligent Systems},
	year = {2021},
	note = {PID2020-119478GB-I00},
	pages = {305{\textendash}315},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Datasets from real-world applications usually deal with many variables and present difficulties when modeling them with traditional classifiers. There is a variety of feature selection and extraction tools that may help with the dimensionality problem, but most of them do not focus on the complexity of the classes. In this paper, a new autoencoder-based model for addressing class complexity in data is introduced, aiming to extract features that present classes in a more separable fashion, thus simplifying the classification task. This is possible thanks to a combination of the standard reconstruction error with a least-squares support vector machine loss function. This model is then applied to a practical use case: classification of chest X-rays according to the presence of COVID-19, showing that learning features that increase linear class separability can boost classification performance. For this purpose, a specific convolutional autoencoder architecture has been designed and trained using the recently published COVIDGR dataset. The proposed model is evaluated by means of several traditional classifiers and metrics, in order to establish the improvements caused by the extracted features. The advantages of using a feature learner and traditional classifiers are also discussed.},
	isbn = {978-3-030-86271-8},
	author = {David Charte and Sevillano-Garc{\'\i}a, Iv{\'a}n and Lucena-Gonz{\'a}lez, Mar{\'\i}a Jes{\'u}s and Mart{\'\i}n-Rodr{\'\i}guez, Jos{\'e} Luis and Francisco Charte and Herrera, Francisco},
	editor = {Sanjurjo Gonz{\'a}lez, Hugo and Pastor L{\'o}pez, Iker and Garc{\'\i}a Bringas, Pablo and Quinti{\'a}n, H{\'e}ctor and Corchado, Emilio}
}
@conference {10.1007/978-3-319-92639-1_23,
	title = {A First Attempt on Monotonic Training Set Selection},
	booktitle = {Hybrid Artificial Intelligent Systems},
	year = {2018},
	pages = {277{\textendash}288},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Monotonicity constraints frequently appear in real-life problems. Many of the monotonic classifiers used in these cases require that the input data satisfy the monotonicity restrictions. This contribution proposes the use of training set selection to choose the most representative instances which improves the monotonic classifiers performance, fulfilling the monotonic constraints. We have developed an experiment on 30 data sets in order to demonstrate the benefits of our proposal.},
	isbn = {978-3-319-92639-1},
	author = {J. R. Cano and S. Garc{\'\i}a},
	editor = {de Cos Juez, Francisco Javier and Villar, Jos{\'e} Ram{\'o}n and de la Cal, Enrique A. and Herrero, {\'A}lvaro and Quinti{\'a}n, H{\'e}ctor and S{\'a}ez, Jos{\'e} Ant{\'o}nio and Corchado, Emilio}
}
@conference {10.1007/978-3-319-32034-2_21,
	title = {Assessment of Multi-Objective Optimization Algorithms for Parametric Identification of a Li-Ion Battery Model},
	booktitle = {Hybrid Artificial Intelligent Systems},
	year = {2016},
	pages = {250{\textendash}260},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {The identification of intelligent models of Li-Ion batteries is a major issue in Electrical Vehicular Technology. On the one hand, the fitness of such models depends on the recursive evaluation of a set of nonlinear differential equations over a representative path in the state space, which is a time consuming task. On the other hand, battery models are intrinsically unstable, and small differences in the initial state or the system, as well as imprecisions in the parameter values, may trigger large differences in the output. Hence, learning battery models from data is a complex multi-modal problem and the parameters of these models must be determined with a high accuracy. In addition to this, producing a dynamical model of a battery is a multi-criteria problem, because the predictive capabilities of the model must be estimated in both the voltage and the temperature domains. In this paper, a selection of state-of-the-art Multi-Objective Optimization Algorithms (SPEA2, NSGA-II, OMOPSO, NSGA-III and MOEA/D) are assessed with regard to their suitability for identifying a model of a Li-Ion battery. The dominance relations that occur between the Pareto fronts are discussed in terms of binary additive {\$}{\$}{\backslash}epsilon {\$}{\$}ϵ-quality indicators. It is concluded that each of the standard implementations of these algorithms has different issues with this particular problem, MOEA/D and NSGA-III being the best overall alternatives.},
	isbn = {978-3-319-32034-2},
	author = {Echevarr{\'\i}a, Yuviny and S{\'a}nchez, Luciano and Blanco, Cecilio},
	editor = {Mart{\'\i}nez-{\'A}lvarez, Francisco and Troncoso, Alicia and Quinti{\'a}n, H{\'e}ctor and Corchado, Emilio}
}
@conference {10.1007/978-3-319-44636-3_29,
	title = {Genetic Fuzzy Modelling of Li-Ion Batteries Through a Combination of Theta-DEA and Knowledge-Based Preference Ordering},
	booktitle = {Advances in Artificial Intelligence},
	year = {2016},
	pages = {310{\textendash}320},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Learning semi-physical fuzzy models of rechargeable Li-Ion batteries from data involves solving a complex multicriteria optimization task where the accuracies of the approximations of the different observable variables are balanced. The fitness function of this problem depends on the recursive evaluation of a set of differential equations, where fuzzy rule-based systems are embedded as nonlinear blocks. Evaluating this function is a time consuming process, thus algorithms that efficiently promote diversity and hence demand a low number of evaluations of the fitness function are preferred. In this paper, a comparison is carried out between some recent genetic algorithms, whose performances are assessed in this particular modelling problem. It is concluded that the combination of the recent {\$}{\$}{\backslash}theta {\$}{\$}-Dominance Evolutionary Algorithm ({\$}{\$}{\backslash}theta {\$}{\$}-DEA) with a Knowledge-based precedence operator, that improves the selection, is a sensible choice. Dominance relations between the Pareto fronts are assessed in terms of binary additive {\$}{\$}{\backslash}epsilon {\$}{\$}-quality indicators.},
	isbn = {978-3-319-44636-3},
	author = {Echevarr{\'\i}a, Yuviny and S{\'a}nchez, Luciano and Blanco, Cecilio},
	editor = {Luaces , Oscar and G{\'a}mez, Jos{\'e} A. and Barrenechea, Edurne and Troncoso, Alicia and Galar, Mikel and Quinti{\'a}n, H{\'e}ctor and Corchado, Emilio}
}
@conference {10.1007/978-3-319-19719-7_23,
	title = {Energy-Efficient Sound Environment Classifier for Hearing Aids Based on Multi-objective Simulated Annealing Programming},
	booktitle = {10th International Conference on Soft Computing Models in Industrial and Environmental Applications},
	year = {2015},
	pages = {261{\textendash}270},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {A methodology for designing classifiers through multicriteria metaheuristics is introduced. The purpose of the new method is to jointly optimize the expected total classification cost and the energy consumption of the device the classifier is implemented on. A numerical study is provided, where different alternatives are implemented on a hearing aid. This aid is capable of automatically classifying the acoustic environment that surrounds the user and choosing the parameters of the amplification that are best adapted to the user{\textquoteright}s comfort. The proposed method attains relevant improvements in energy consumption with small to negligible loss in classification accuracy with respect to a selection of algorithms.},
	isbn = {978-3-319-19719-7},
	author = {Coca{\~n}a-Fern{\'a}ndez, Alberto and S{\'a}nchez, Luciano and Ranilla, Jos{\'e} and Gil-Pita, Roberto and Ayll{\'o}n, David},
	editor = {Herrero, {\'A}lvaro and Sedano, Javier and Baruque, Bruno and Quinti{\'a}n, H{\'e}ctor and Corchado, Emilio}
}
@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}
}
