@conference {307,
	title = {A First Approach to Face Dimensionality Reduction Through Denoising Autoencoders},
	booktitle = {19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018},
	year = {2018},
	note = {TIN2015-68854-R; FPU16/00324},
	month = {11},
	pages = {439{\textendash}447},
	address = {Madrid (Spain)},
	abstract = {The problem of high dimensionality is a challenge when facing machine learning tasks. A high dimensional space has a negative effect on the predictive performance of many methods, specifically, classification algorithms. There are different proposals that arise to mitigate the effects of this phenomenon. In this sense, models based on deep learning have emerged.},
	isbn = {978-3-030-03493-1},
	doi = {10.1007/978-3-030-03493-1_46},
	author = {F. Pulgar-Rubio and Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus}
}
@article {386,
	title = {AEkNN: An AutoEncoder kNN-Based Classifier With Built-in Dimensionality Reduction},
	journal = {International Journal of Computational Intelligence Systems},
	volume = {12},
	year = {2018},
	note = {TIN2015-68854-R; FPU16/00324},
	month = {11/2018},
	pages = {436-452},
	abstract = {High dimensionality tends to be a challenge for most machine learning tasks, including classification. There are different classification methodologies, of which instance-based learning is one. One of the best known members of this family is the k-nearest neighbors (kNNs) algorithm. Its strategy relies on searching a set of nearest instances. In high-dimensional spaces, the distances between examples lose significance. Therefore, kNN, in the same way as many other classifiers, tends to worsen its performance as the number of input variables grows. In this study, AEkNN, a new kNN-based algorithm with built-in dimensionality reduction, is presented. Aiming to obtain a new representation of the data, having a lower dimensionality but with more informational features, AEkNN internally uses autoencoders. From this new vector of features the computed distances should be more significant, thus providing a way to choose better neighbors. An experimental evaluation of the new proposal is conducted, analyzing several configurations and comparing them against the original kNN algorithm and classical dimensionality reduction methods. The obtained conclusions demonstrate that AEkNN offers better results in predictive and runtime performance.},
	issn = {1875-6883},
	doi = {10.2991/ijcis.2019.0025},
	url = {https://www.atlantis-press.com/article/125905686},
	author = {F. Pulgar-Rubio and Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus}
}
@conference {331,
	title = {An{\'a}lisis del impacto de datos desbalanceados en el rendimiento predictivo de redes neuronales convolucionales},
	booktitle = {XVIII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (CAEPIA 2018)},
	year = {2018},
	note = {TIN2015-68454-R; FPU16/00324},
	month = {10},
	pages = {1213{\textendash}1218},
	address = {Granada (Spain)},
	abstract = {En los {\'u}ltimos a{\~n}os han surgido nuevas propuestas basadas en Deep Learning para afrontar la tarea de clasificaci{\'o}n. Estas propuestas han obtenido buenos resultados en algunos campos, por ejemplo, en reconocimiento de im{\'a}genes. Sin embargo, existen factores que deben ser analizados para valorar su influencia en los resultados obtenidos con estos nuevos modelos. En este trabajo se analiza la clasificaci{\'o}n de datos desbalanceados con redes neuronales convolucionales (Convolutional Neural Networks-CNNs). Para hacerlo, se han llevado a cabo una serie de tests donde se reconocen im{\'a}genes mediante CNNs. As{\'\i}mismo, se utilizan conjuntos de datos con diferente grado de desbalanceo. Los resultados demuestran que el desequilibrio afecta negativamente al rendimiento predictivo.},
	isbn = {978-88-61970-00-7},
	author = {F. Pulgar-Rubio and A.J. Rivera-Rivas and Francisco Charte and Mar{\'\i}a J del Jesu D{\'\i}az}
}
@conference {309,
	title = {A Transformation Approach Towards Big Data Multilabel Decision Trees},
	booktitle = {14th International Work-Conference on Artificial Neural Networks (IWANN 2017)},
	year = {2017},
	note = {TIN2015-68454-R},
	month = {6},
	pages = {73{\textendash}84},
	address = {C{\'a}diz (Spain)},
	abstract = {A large amount of the data processed nowadays is multilabel in nature. This means that every pattern usually belongs to several categories at once. Multilabel data are abundant, and most multilabel datasets are quite large. This causes that many multilabel classification methods struggle with their processing. Tackling this task by means of big data methods seems a logical choice. However, this approach has been scarcely explored by now. The present work introduces several big data multilabel classifiers, all of them based on decision trees. After detailing how they have been designed, their predictive performance, as well as the execution time, are analyzed.},
	isbn = {978-3-319-59152-0},
	doi = {10.1007/978-3-319-59153-7_7},
	author = {A.J. Rivera-Rivas and Francisco Charte and F. Pulgar-Rubio and M. J. del Jesus}
}
@article {602,
	title = {MEFASD-BD: Multi-Objective Evolutionary Algorithm for Subgroup Discovery in Big Data Environments - A MapReduce Solution},
	journal = {Knowledge-Based Systems},
	volume = {117},
	year = {2017},
	note = {TIN2015-68454-R},
	pages = {70-78},
	doi = {http://dx.doi.org/10.1016/j.knosys.2016.08.021},
	author = {F. Pulgar-Rubio and A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy and P. Gonz{\'a}lez and C. J. Carmona and M. J. del Jesus}
}
@conference {311,
	title = {On the Impact of Imbalanced Data in Convolutional Neural Networks Performance},
	booktitle = {12th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2017},
	year = {2017},
	note = {TIN2015-68454-R},
	month = {6},
	pages = {220{\textendash}232},
	address = {La Rioja (Spain)},
	abstract = {In recent years, new proposals have emerged for tackling the classification problem based on Deep Learning (DL) techniques. These proposals have shown good results in certain fields, such as image recognition. However, there are factors that must be analyzed to determine how they influence the results obtained by these new algorithms. In this paper, the classification of imbalanced data with convolutional neural networks (CNNs) is analyzed. To do this, a series of tests will be performed in which the classification of real images of traffic signals by CNNs will be performed based on data with different imbalance levels.},
	isbn = {978-3-319-59650-1},
	doi = {10.1007/978-3-319-59650-1_19},
	author = {F. Pulgar-Rubio and A.J. Rivera-Rivas and Francisco Charte and M. J. del Jesus}
}
@article {300,
	title = {Uso de dispositivos FPGA como apoyo a la ense{\~n}anza de asignaturas de arquitectura de computadores},
	journal = {Ense{\~n}anza y aprendizaje de ingenier{\'\i}a de computadores. Revista de experiencias docentes en ingenier{\'\i}a de computadores},
	volume = {7},
	year = {2017},
	note = {-},
	pages = {37{\textendash}52},
	abstract = {Computer Engineering students in Spanish universities have to take one or more courses devoted to theur learning of computer architecture. The theoretical part of these subjects are usually focused on describing the architecture itself, while practical sessions are used to introduce assembly programming by means of a certain instruction set which runs into a software emulator. This paper proposes to supplement practical sessions, so that students learn to design a microprocessor by themselves from its basic components, by introducing the use of FPGA devices.},
	issn = {2173-8688},
	author = {Francisco Charte and Espinilla, Macarena and A.J. Rivera-Rivas and F. Pulgar-Rubio}
}
@conference {600,
	title = {Estimating the Maximum Power Delivered by Concentrating Photovoltaics Technology Through Atmospheric Conditions Using a Differential Evolution Approach},
	booktitle = {Proceedings of the Eleventh International Conference on Hybrid Artificial Intelligence Systems (HAIS)},
	year = {2016},
	note = {ENE2009-08302, P09-TEP-5045, TIN2015-68454-R},
	month = {April},
	pages = {273-282},
	publisher = {Springer},
	organization = {Springer},
	address = {Sevilla (Spain)},
	author = {C. J. Carmona and F. Pulgar-Rubio and A.J. Rivera-Rivas and M. J. del Jesus and J. Aguilera}
}
@article {301,
	title = {Explotaci{\'o}n de la potencia de procesamiento mediante paralelismo: un recorrido hist{\'o}rico hasta la GPGPU},
	journal = {Ense{\~n}anza y aprendizaje de ingenier{\'\i}a de computadores. Revista de experiencias docentes en ingenier{\'\i}a de computadores},
	volume = {6},
	year = {2016},
	note = {-},
	pages = {19{\textendash}33},
	abstract = {Due to the improvement of semiconductor manufacturing technologies, and higher integration scales in the last decades, the power of computing devices has experienced an impressive growth. However, some obstacles have been also found along the way. As a consequence, the battle for reaching higher clock frequencies almost ended a few years ago. Nowadays, the power of processors is not measured exclusively using GHz. Other factors, as the number of cores and their inner design, also have a large impact. This paper provides an historical review on how parallelism techniques have been adapted over time to overcome these changes aiming to better exploit the available hardware.},
	issn = {2173-8688},
	author = {Francisco Charte and A.J. Rivera-Rivas and F. Pulgar-Rubio and Mar{\'\i}a J del Jesu D{\'\i}az}
}
@conference {317,
	title = {An ensemble strategy for forecasting the extra-virgin olive oil price in Spain},
	booktitle = {International work-conference on Time Series, ITISE 2015},
	year = {2015},
	note = {TIN2012-33856},
	month = {7},
	pages = {506{\textendash}516},
	address = {Granada (Spain)},
	abstract = {Time series prediction is one of the key tasks in data mining, especially in areas such as science, engineering and business. It is possible to distinguish between fundamental analysis and technical analysis while dealing with time series in the business area. Fundamental analysis takes into account different exogenous variables such as expenses, assets or liabilities. Technical analysis summarizes information using technical indicators such as momentums, moving averages or oscillators. The most influential exogenous variables and technical indicators for the olive oil price have been already identified in previous studies. The objective of the present paper is to propose an ensemble strategy, based on dividing this set of exogenous variables and technical indicators into subsets of features for the base models. These base models use CO2RBFN, a cooperative competitive algorithm for RBFNs, as learning algorithm. The obtained results show that the ensemble strategy outperforms both the base models and other classical soft computing methods.},
	isbn = {978-84-16292-20-2},
	author = {A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy and Francisco Charte and F. Pulgar-Rubio and M. J. del Jesus}
}
@conference {Cpggj15,
	title = {An{\'a}lisis descriptivo mediante aprendizaje supervisado basado en patrones emergentes},
	booktitle = {VII Simposio de Teor{\'\i}a y Aplicaciones de Miner{\'\i}a de Datos},
	year = {2015},
	note = {TIN2012-33856},
	pages = {685-694},
	author = {C. J. Carmona and F. Pulgar-Rubio and A.M. Garc{\'\i}a-Vico and P. Gonz{\'a}lez and M. J. del Jesus}
}
@conference {Pcagd15,
	title = {Una primera aproximaci{\'o}n al descubrimiento de subgrupos bajo el paradigma MapReduce},
	booktitle = {1er Workshop en Big Data y An{\'a}lisis de Datos Escalable},
	year = {2015},
	note = {TIN2012-33856},
	pages = {991-1000},
	author = {F. Pulgar-Rubio and C. J. Carmona and A.J. Rivera-Rivas and P. Gonz{\'a}lez and M. J. del Jesus}
}
