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Book

Multilabel Classification: Problem Analysis, Metrics and Techniques, Herrera, F., Charte Francisco, Rivera-Rivas A.J., and del Jesus M. J. , (2016)

Book Chapter

CO2RBFN for Short and Medium Term Forecasting of the Extra Virgin Olive Oil Price, Pérez-Godoy, M.D., Pérez P., Frías M.P., Rivera-Rivas A.J., Carmona C. J., and Parras M. , Studies in Computational Intelligence, Volume 284, p.113-125, (2010)

2010 - NICSO.pdf (589.38 KB)

PRESETEMP: Predicción de Series Temporales mediante técnicas de Minería de Datos, Rivas, V. M., Parras-Gutiérrez E., Pérez-Godoy M.D., Rivera-Rivas A.J., Olmo M. J., Arenas M.G., Fernandez-Montoya A., and Cillero M. , Proyectos de Investigación 2009-10, (2012)

Conference Paper

A first analysis of the effect of local and global optimization weights methods in the cooperative-competitive design of RBFN for imbalanced environments, Pérez-Godoy, M.D., Rivera-Rivas A.J., del Jesus M. J., and Martínez Francisco , The 2013 International Joint Conference on Neural Networks (IJCNN), Aug, p.1-8, (2013)

A First Approach to Deal with Imbalance in Multi-label Datasets, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , 8th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2013), 9, Salamanca (Spain), p.150-160, (2013)

2013-HAIS-ImbalanceMultilabel.pdf (194.4 KB)

A First Approach to Face Dimensionality Reduction Through Denoising Autoencoders, Pulgar-Rubio, F., Charte Francisco, Rivera-Rivas A.J., and del Jesus M. J. , 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018, 11, Madrid (Spain), p.439–447, (2018)

2018-IDEAL-DimensionalityDAE.pdf (2.45 MB)

A First Approximation to the Effects of Classical Time Series Preprocessing Methods on LSTM Accuracy, Viedma, Daniel Trujillo, Rivera-Rivas A.J., Charte Francisco, and del Jesus M. J. , International Work-Conference on Artificial Neural Networks, 05/2019, p.270-280, (2019)

A Performance Study of Concentrating Photovoltaic Modules Using Neural Networks: An Application with CO2RBFN, Rivera-Rivas, A.J., García-Domingo B., del Jesus M. J., and Aguilera J. , Soft Computing Models in Industrial and Environmental Applications, Berlin, Heidelberg, p.439–448, (2013)

A Preliminar Analysis of CO2RBFN in Imbalanced Problems, Pérez-Godoy, M.D., Rivera-Rivas A.J., Fernández A., del Jesus M. J., and Herrera F. , Bio-Inspired Systems: Computational and Ambient Intelligence, Berlin, Heidelberg, p.57–64, (2009)

A Preliminary Study on Crop Classification with Unsupervised Algorithms for Time Series on Images with Olive Trees and Cereal Crops , Rivera-Rivas, A.J., Pérez-Godoy M.D., Elizondo D., Deka Lipika, and del Jesus M. J. , 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020), 08/2020, p.276-285, (2020)

A Preliminary Study on Mutation Operators in Cooperative Competitive Algorithms for RBFN Design, Pérez-Godoy, M.D., Rivera-Rivas A.J., Carmona C. J., and del Jesus M. J. , IEEE World Congress on Computational Intelligence (WCCI), p.349–355, (2010)

2010 - IEEEWCCS - CoreA.pdf (606.64 KB)

A specialized lazy learner for time series forecasting, Martínez, Francisco, Frías M.P., Charte Francisco, and Rivera-Rivas A.J. , 17th International Conference on Computational and Mathematical Methods in Science and Engineering, CMMSE 2017, 7, Costa Ballena, Rota, Cáadiz (Spain), p.1397–1403, (2017)

2017-CMMSE-SpecializedLazyLearner.pdf (211.11 KB)

A Summary on the Study of the Medium-Term Forecasting of the Extra-Virgen Olive Oil Price, Rivera-Rivas, A.J., Pérez-Godoy M.D., del Jesus M. J., Pérez-Recuerda Pedro, Frías María Pilar, and Parras Manuel , Advances in Artificial Intelligence, Berlin, Heidelberg, p.263–272, (2011)

A Transformation Approach Towards Big Data Multilabel Decision Trees, Rivera-Rivas, A.J., Charte Francisco, Pulgar-Rubio F., and del Jesus M. J. , 14th International Work-Conference on Artificial Neural Networks (IWANN 2017), 6, Cádiz (Spain), p.73–84, (2017)

2017-IWANN-BigDataMLDT.pdf (372.37 KB)

Adaptación de una asignatura avanzada de redes de computadores al modelo de docencia virtual dentro del marco del Espacio Europeo de Educación Superior, Rivera-Rivas, A.J., Carmona C. J., Pérez-Godoy M.D., and del Jesus M. J. , International Conference on Development and Innovation with New Technologies in Engineering Education, p.15-21, (2009)

2009 - FINDIT.pdf (168.85 KB)

Alternative OVA Proposals for Cooperative Competitive RBFN Design in Classification Tasks, Charte, Francisco, Rivera-Rivas A.J., Pérez-Godoy M.D., and del Jesus M. J. , 12th International Work-Conference on Artificial Neural Networks (IWANN 2013), Tenerife (Spain), p.331-338, (2013)

2013-IWANN-AlternativeOVA.pdf (146.42 KB)

An Approximation to Deep Learning Touristic-Related Time Series Forecasting, Trujillo, Daniel, Rivera-Rivas A.J., Charte Francisco, and del Jesus M. J. , 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018, 11, Madrid (Spain), p.448–456, (2018)

2018-IDEAL-LSTMTouristic.pdf (2.13 MB)

An ensemble method for time series forecasting with simple exponential smoothing, del Jesus, M. J., Martínez Francisco, Pérez-Godoy M.D., Rivera-Rivas A.J., and Frías María Pilar , Conference Computational and Mathematical Methods in Science and Engineering, 07, Rota, Cádiz (Spain), (2014)

An ensemble strategy for forecasting the extra-virgin olive oil price in Spain, Rivera-Rivas, A.J., Pérez-Godoy M.D., Charte Francisco, Pulgar-Rubio F., and del Jesus M. J. , International work-conference on Time Series, ITISE 2015, 7, Granada (Spain), p.506–516, (2015)

2015-ITISE-ForecastOliveOil.pdf (547.65 KB)

An Study on Data Mining Methods for Short-Term Forecasting of the Extra Virgin Olive Oil Price in the Spanish Market, Pérez, P., Frías M. P., Pérez-Godoy M.D., Rivera-Rivas A.J., Jesus M. J. d., Parras M., and Torres F. J. , 2008 Eighth International Conference on Hybrid Intelligent Systems, Sep., p.943-946, (2008)

Análisis del impacto de datos desbalanceados en el rendimiento predictivo de redes neuronales convolucionales, Pulgar-Rubio, F., Rivera-Rivas A.J., Charte Francisco, and Díaz María J. del Jesu , XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2018), 10, Granada (Spain), p.1213–1218, (2018)

2018-CAEPIA-DesbalanceoCNNs.pdf (228.43 KB)

Aproximaciones de Evolución con Funciones Difusas Mediante Cooperacion y Competición de RBFs, Rivera-Rivas, A.J., Ortega Julio, Díaz María José del, and González-Peñalvez Jesús , Congreso Español de Algoritmos Evolutivos y Bioinspirados AEB-02, 01, Mérida, (España), (2002)

Automatic Time Series Forecasting with GRNN: A Comparison with Other Models, Martínez, Francisco, Charte Francisco, Rivera-Rivas A.J., and Frías María Pilar , International Work-Conference on Artificial Neural Networks, 05/2019, p.198-209, (2019)

Automating Autoencoder Architecture Configuration: An Evolutionary Approach, Charte, Francisco, Rivera-Rivas A.J., Martínez Francisco, and del Jesus M. J. , International Work-Conference on the Interplay Between Natural and Artificial Computation, 05/2019, p.339-349, (2019)

CO2RBFN-CS: First Approach Introducing Cost-Sensitivity in the Cooperative-Competitive RBFN Design, Pérez-Godoy, M.D., Rivera-Rivas A.J., Charte Francisco, and del Jesus M. J. , 13th International Work-Conference on Artificial Neural Networks (IWANN 2015), 6, Palma de Mallorca (Spain), p.361–373, (2015)

Concurrence among Imbalanced Labels and Its Influence on Multilabel Resampling Algorithms, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , 9th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2014), 6, Salamanca (Spain), p.110–121, (2014)

2014-HAIS-ConcurrenceLabels.pdf (1.12 MB)

EMORBFN: An Evolutionary Multiobjetive Optimization Algorithm for RBFN Design, López, P.L., Rivera-Rivas A.J., Pérez-Godoy M.D., del Jesus M. J., and Carmona C. J. , International Work-Conference on Artificial Neural Networks (IWANN), Number 2009, p.752–759, (2009)

2009 - IWANN - CoreB.pdf (190.56 KB)

Estimating the Maximum Power Delivered by Concentrating Photovoltaics Technology Through Atmospheric Conditions Using a Differential Evolution Approach, Carmona, C. J., Pulgar-Rubio F., Rivera-Rivas A.J., del Jesus M. J., and Aguilera J. , Proceedings of the Eleventh International Conference on Hybrid Artificial Intelligence Systems (HAIS), April, Sevilla (Spain), p.273-282, (2016)

2016 - HAIS - CoreC.pdf (203.25 KB)

Estudio de las Fases de un Algoritmo de Optimización para Redes de Funciones de Base Radial, Rivera-Rivas, A.J., Rojas I., and J. Lopera Ortega , Actas del Simposio de Inteligencia Computacional (SICO), September, Granada. Spain, p.237-244, (2005)

Herramientas para el desarrollo de prácticas en una asignatura de redes de computadores de una ingeniería técnica, Rivera-Rivas, A.J., and Pérez-Godoy M.D. , Jornadas de informática, Cádiz (España), (1997)

Improving Multi-label Classifiers via Label Reduction with Association Rules, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , 7th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2012), 9, Salamanca (Spain), p.188–199, (2012)

2012-HAIS-LabelReduction.pdf (171.47 KB)

Intelligent Systems in Long-Term Forecasting of the Extra-Virgin Olive Oil Price in the Spanish Market, Pérez-Godoy, M.D., Pérez Pedro, Rivera-Rivas A.J., del Jesus M. J., Frías María Pilar, and Parras Manuel , Trends in Applied Intelligent Systems, Berlin, Heidelberg, p.205–214, (2010)

La asignatura de planificación de sistemas informáticos en ingeniería técnica en informática de gestión., González, P., Pérez-Godoy M.D., and Rivera-Rivas A.J. , Jenui, 01, Alcala de Henares (España), (2000)

Mejoras en el Diseño Multiobjetivo de Redes de Funciones de Base Radial, López, P.L., Rivera-Rivas A.J., Carmona C. J., and Pérez-Godoy M.D. , Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF), p.441-446, (2010)

2010 - ESTYLF.pdf (587.96 KB)

MLeNN: A First Approach to Heuristic Multilabel Undersampling, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , 15th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2014, 9, Salamanca (Spain), p.1-9, (2014)

2014-IDEAL-MLeNN.pdf (184.04 KB)

MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , XVII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2016), 9, Salamanca (Spain), p.821–822, (2016)

2016-CAEPIA-MLSMOTE.pdf (579.04 KB)

Modeling the Transformation of Olive Tree Biomass into Bioethanol with Reg-CO2RBFN, Charte, Francisco, Romero Inmaculada, Rivera-Rivas A.J., and Castro Eulogio , 14th International Work-Conference on Artificial Neural Networks (IWANN 2017), 6, Cádiz (Spain), p.733–744, (2017)

2017IWANN-Biomass.pdf (158.82 KB)

Multi-label Testing for CO2RBFN: A First Approach to the Problem Transformation Methodology for Multi-label Classification, Rivera-Rivas, A.J., Charte Francisco, Pérez-Godoy M.D., and del Jesus M. J. , 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, 6, Torremolinos-Málaga (Spain), p.41–48, (2011)

2011-IWANN-MultilabelTestingCO2RBFN.pdf (131.76 KB)

On the Impact of Dataset Complexity and Sampling Strategy in Multilabel Classifiers Performance, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , 11th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2016, 4, Seville (Spain), p.500–511, (2016)

Complexity.pdf (1.56 MB)

On the Impact of Imbalanced Data in Convolutional Neural Networks Performance, Pulgar-Rubio, F., Rivera-Rivas A.J., Charte Francisco, and del Jesus M. J. , 12th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2017, 6, La Rioja (Spain), p.220–232, (2017)

Pulgar2017_Chapter_OnTheImpactOfImbalancedDataInC.pdf (634.69 KB)

Optimización de redes de RBFs mediante cooperación-competición de neuronas y algoritmos de minimización de error, Rivera-Rivas, A.J., Rojas I., J. Lopera Ortega, and del Jesus M. J. , MAEB, February, p.499-508, (2003)

Predicción de series temporales mediante la coevolución de funciones base, Rivera-Rivas, A.J., Rojas I., and J. Lopera Ortega , Tercer Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados MAEB, February, p.585-592, (2004)

Presentación de TCL-TK para el desarrollo de aplicaciones por parte de usuarios no expertos en programación, González, P., Rivera-Rivas A.J., and Pérez-Godoy M.D. , Jornadas científicas andaluzas en tecnología de la información, Cádiz (España), (1998)

{QUINTA: A question tagging assistant to improve the answering ratio in electronic forums}, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , IEEE International Conference on Computer as a Tool, EUROCON 2015, 9, Salamanca (Spain), p.1-6, (2015)

2015-EUROCON-QUINTA.pdf (2.03 MB)

R Ultimate Multilabel Dataset Repository, Charte, Francisco, Charte David, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , 11th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2016, 4, Seville (Spain), p.487–499, (2016)

RUMDR.pdf (294.87 KB)

Recognition of Activities in Resource Constrained Environments; Reducing the Computational Complexity, Espinilla, M., Rivera-Rivas A.J., Pérez-Godoy M.D., Medina J., Martínez L., and Nugent C. , Ubiquitous Computing and Ambient Intelligence, Cham, p.64–74, (2016)

Resampling Multilabel Datasets by Decoupling Highly Imbalanced Labels, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , 10th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2015, 6, Bilbao (Spain), p.489–501, (2015)

2015-HAIS-REMEDIAL.pdf (1.04 MB)

Un sistema coordinador de recursos distribuidos, López, Juan Antonio, Balsas José R., and Rivera-Rivas A.J. , Jornadas científicas en tecnologías de la información, 11, Cádiz (España), (2000)

Una estrategia difusa para la aplicación de operadores en un algoritmo evolutivo, Rivera-Rivas, A.J., Rojas I., J. Lopera Ortega, and del Jesus M. J. , XII Congreso Español sobre Tecnologías y Lógica Fuzzy(ESTYLF), September, Jaén, p.593-598, (2004)

Una primera aproximación a la predicción de variables turísticas con Deep Learning, Viedma, Daniel Trujillo, Rivera-Rivas A.J., Charte Francisco, and Díaz María J. del Jesu , XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2018), 10, Granada (Spain), p.939–943, (2018)

2018-CAEPIA-TurismoLSTMs.pdf (127.66 KB)

Una primera aproximación al descubrimiento de subgrupos bajo el paradigma MapReduce, Pulgar-Rubio, F., Carmona C. J., Rivera-Rivas A.J., González P., and del Jesus M. J. , 1er Workshop en Big Data y Análisis de Datos Escalable, p.991-1000, (2015)

2015 - BD.pdf (374.17 KB)

Una propuesta para la formación en las tecnologías Web, Rivera-Rivas, A.J., and Balsas José R. , Nuevas tecnologías aplicadas a la educación, 11, (2000)

Journal Article

A differential evolution proposal for estimating the maximum power delivered by CPV modules under real outdoor conditions, García-Domingo, B., Carmona C. J., Rivera-Rivas A.J., del Jesus M. J., and Aguilera J. , Expert Systems with Applications, Volume 42, Number 13, p.5452–5462, (2015)

2015-GarciaDomingo-ESWA.pdf (1.22 MB)

A methodology for applying k-nearest neighbor to time series forecasting, Martínez, Francisco, Frías María Pilar, Pérez-Godoy M.D., and Rivera-Rivas A.J. , Artificial Intelligence Review, Nov, (2017)

A new hybrid methodology for cooperative-coevolutionary optimization of radial basis function networks, Rivera-Rivas, A.J., Rojas I., Ortega J., and del Jesus M. J. , Soft Computing, May, Volume 11, Number 7, p.655–668, (2007)

A study on the medium-term forecasting using exogenous variable selection of the extra-virgin olive oil with soft computing methods, Rivera-Rivas, A.J., Pérez-Recuerda Pedro, Pérez-Godoy M.D., del Jesus M. J., Frías María Pilar, and Parras Manuel , Applied Intelligence, Jun, Volume 34, Number 3, p.331–346, (2011)

Addressing imbalance in multilabel classification: Measures and random resampling algorithms, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , Neurocomputing, Volume 163, p.3–16, (2015)

2015-Neucom-AddressingImbalance.pdf (546.81 KB)

AEkNN: An AutoEncoder kNN-Based Classifier With Built-in Dimensionality Reduction, Pulgar-Rubio, F., Charte Francisco, Rivera-Rivas A.J., and del Jesus M. J. , International Journal of Computational Intelligence Systems, 11/2018, Volume 12, p.436-452, (2018)

125905686.pdf (3.86 MB)

Choosing the proper autoencoder for feature fusion based on data complexity and classifiers: Analysis, tips and guidelines, Pulgar, Francisco J., Charte Francisco, Rivera-Rivas A.J., and del Jesus M. J. , Information Fusion, 02/2020, Volume 54, p.44-60, (2020)

1-s2.0-S1566253519300880-main.pdf (895.14 KB)

ClEnDAE: A classifier based on ensembles with built-in dimensionality reduction through denoising autoencoders, Pulgar, Francisco J., Charte Francisco, Rivera-Rivas A.J., and del Jesus M. J. , Information Sciences, Volume 565, p.146-176, (2021)

1-s2.0-S0020025521002024-main.pdf (1.79 MB)

CO2RBFN for short-term forecasting of the extra virgin olive oil price in the Spanish market, Pérez-Godoy, M.D., Pérez P., Rivera-Rivas A.J., del Jesus M. J., Carmona C. J., Frías M.P., and Parras M. , International Journal of Hybrid Intelligent Systems, p.75-87, (2010)

2010-Perez-Godoy-IJHIS.pdf (434.9 KB)

Comparative analysis of data mining and response surface methodology predictive models for enzymatic hydrolysis of pretreated olive tree biomass, Charte, Francisco, Romero Inmaculada, Pérez-Godoy M.D., Rivera-Rivas A.J., and Castro Eulogio , Computers & Chemical Engineering, Volume 101, p.23–30, (2017)

2017-CACE-Biomasa.pdf (1.28 MB)

Dealing with difficult minority labels in imbalanced mutilabel data sets, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , Neurocomputing, Volume 326, p.39–53, (2019)

2019-NeucomDealingDifficultLabels.pdf (4.77 MB)

Dealing with seasonality by narrowing the training set in time series forecasting with kNN, Martínez, Francisco, Frías María Pilar, Pérez-Godoy M.D., and Rivera-Rivas A.J. , Expert Systems with Applications, Volume 103, p.38 - 48, (2018)

El ecosistema de aprendizaje del estudiante universitario en la post-pandemia. Metodologías y herramientas, Charte, Francisco, Rivera-Rivas A.J., Medina J., and Espinilla Macarena , Enseñanza y Aprendizaje de Ingeniería de Computadores, Number 10, (2020)

EvoAAA: An evolutionary methodology for automated neural autoencoder architecture search, Charte, Francisco, Rivera-Rivas A.J., Martínez Francisco, and del Jesus M. J. , Integrated Computer-Aided Engineering, 05/2020, Volume 27, Number 3, p.211-231, (2020)

FCharte-EvoAAAOpen.pdf (820.83 KB)

Evolución tecnológica del hardware de vídeo y las GPU en los ordenadores personales, Charte, Francisco, Rueda Antonio J., Espinilla Macarena, and Rivera-Rivas A.J. , Enseñanza y aprendizaje de ingeniería de computadores. Revista de experiencias docentes en ingeniería de computadores, Volume 7, p.111–128, (2017)

2017-EAIC17-EvolucionGPUs.pdf (2.1 MB)

Explotación de la potencia de procesamiento mediante paralelismo: un recorrido histórico hasta la GPGPU, Charte, Francisco, Rivera-Rivas A.J., Pulgar-Rubio F., and Díaz María J. del Jesu , Enseñanza y aprendizaje de ingeniería de computadores. Revista de experiencias docentes en ingeniería de computadores, Volume 6, p.19–33, (2016)

2016-EAIC16-Paralelismo.pdf (1.16 MB)

Gamificación en procesos de autoentrenamiento y autoevaluación. Experiencia en la asignatura de Arquitectura de Computadores, Espinilla, M., Santamaría J, and Rivera-Rivas A.J. , Volume 6, p.55-65, (2016)

GP-COACH: Genetic Programming-based learning of Compact and ACcurate fuzzy rule-based classification systems for High-dimensional problems, Berlanga, F.J., Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , Information Sciences, Volume 180, Number 8, p.1183 - 1200, (2010)

LI-MLC: A Label Inference Methodology for Addressing High Dimensionality in the Label Space for Multilabel Classification, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , IEEE Transactions on Neural Networks and Learning Systems, Volume 25, Number 10, p.1842-1854, (2014)

2014-TNNLS-LI-MLC.pdf (1.85 MB)

MEFASD-BD: Multi-Objective Evolutionary Algorithm for Subgroup Discovery in Big Data Environments - A MapReduce Solution, Pulgar-Rubio, F., Rivera-Rivas A.J., Pérez-Godoy M.D., González P., Carmona C. J., and del Jesus M. J. , Knowledge-Based Systems, Volume 117, p.70-78, (2017)

MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , Knowledge-Based Systems, Volume 89, p.385–397, (2015)

2015-KBS-MLSMOTE.pdf (1.56 MB)

Nuevas arquitecturas hardware de procesamiento de alto rendimiento para aprendizaje profundo, Rivera-Rivas, A.J., Charte Francisco, Espinilla Macarena, and Pérez-Godoy M.D. , Enseñanza y aprendizaje de ingeniería de computadores. Revista de experiencias docentes en ingeniería de computadores, Volume 8, p.67–83, (2018)

2018-EAIC18-HardwareAltoRend-compressed.pdf (431.68 KB)

predtoolsTS: R package for streamlining time series forecasting, Charte, Francisco, Vico Alberto, Pérez-Godoy M.D., and Rivera-Rivas A.J. , Progress in Artificial Intelligence, 06/2019, Volume 8, p.505–510, (2019)

Charte2019_Article_PredtoolsTSRPackageForStreamli.pdf (390.07 KB)

Propuesta de una asignatura de Diseño de Servidores para la especialidad de Tecnologías de Información, Rivera-Rivas, A.J., Espinilla Macarena, Fernández A., López José Santamarí, and Charte Francisco , Enseñanza y aprendizaje de ingeniería de computadores. Revista de experiencias docentes en ingeniería de computadores, Volume 4, p.15–24, (2014)

2014-EAIC14-AsignaturaDisenoServ.pdf (864.57 KB)

REMEDIAL-HwR: Tackling multilabel imbalance through label decoupling and data resampling hybridization, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , Neurocomputing, Volume 326, p.110–122, (2019)

2019-NeucomRemedial-HwR.pdf (2.83 MB)

Time Series Forecasting with KNN in R: the tsfknn Package, Martínez, Francisco, Frías María Pilar, Charte Francisco, and Rivera-Rivas A.J. , The R Journal, 12/2019, Volume 11, Number 2, p.229-242, (2019)

RJ-2019-004.pdf (204.01 KB)

Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository, Charte, Francisco, Rivera-Rivas A.J., Charte David, del Jesus M. J., and Herrera F. , Neurocomputing, Volume 289, p.68–85, (2018)

2018-Neucom-TipsMLCCometa-compressed.pdf (1017.86 KB)

Training algorithms for Radial Basis Function Networks to tackle learning processes with imbalanced data-sets, Pérez-Godoy, M.D., Rivera-Rivas A.J., Carmona C. J., and del Jesus M. J. , Applied Soft Computing, Volume 25, p.26-39, (2014)

2014-Perez-ASOC.pdf (926.82 KB)

Uso de dispositivos FPGA como apoyo a la enseñanza de asignaturas de arquitectura de computadores, Charte, Francisco, Espinilla Macarena, Rivera-Rivas A.J., and Pulgar-Rubio F. , Enseñanza y aprendizaje de ingeniería de computadores. Revista de experiencias docentes en ingeniería de computadores, Volume 7, p.37–52, (2017)

2017-EAIC17-FPGAsEnsenanza.pdf (1.48 MB)