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Book

El pasado de la computación personal. Historia de la microinformática (2a Edición), Charte, Francisco, and García Lina , (2019)

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

Conference Paper

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 approach towards a fuzzy decision tree for multilabel classification, Prati, Ronaldo C., Charte Francisco, and Herrera F. , 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 7, Naples (Italy), p.1–6, (2017)

2017-IEEE-FuzzyDTMLC.pdf (299.08 KB)

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 practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines, Charte, David, Charte Francisco, García S., del Jesus M. J., and Herrera F. , XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2018), 10, Granada (Spain), p.949–950, (2018)

2018-CAEPIA-TutorialAEs.pdf (59.39 KB)

A Showcase of the Use of Autoencoders in Feature Learning Applications, Charte, David, Charte Francisco, del Jesus M. J., and Herrera F. , International Work-Conference on the Interplay Between Natural and Artificial Computation, 05/2019, p.412-421, (2019)

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 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)

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 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)

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)

Análisis visual de técnicas no supervisadas de deep learning con el paquete dlvisR, Charte, David, Charte Francisco, and Herrera F. , XVII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2016), 9, Salamanca (Spain), p.895–904, (2016)

2016-CAEPIA-dlvisR.pdf (2.56 MB)

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)

2015-IWANN-CostSensitiveCO2RBFN.pdf (360.2 KB)

Combining simple exponential smoothing models for time series forecasting, Martínez, Francisco, Pérez-Godoy M.D., Charte Francisco, and del Jesus M. J. , International work-conference on Time Series, ITISE 2016, 6, Granada (Spain), p.635-644, (2016)

2016-ITISE-ExponentialSmoothing.pdf (1.13 MB)

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)

E2PAMEA: un algoritmo evolutivo para la extraccióni eficiente de patrones emergentes difusos en entornos big data, García-Vico, A.M., Elizondo D., Charte Francisco, González P., and Carmona C. J. , Proceedings of the XIX Conference of the Spanish Association for Artificial Intelligence, (2021)

2021 - CAEPIA - E2PM.pdf (173.29 KB)

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)

mldr: Paquete R para Exploración de Datos Multietiqueta, Charte, David, and Charte Francisco , XVI Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2015), 11, Albacete (Spain), p.695–704, (2015)

2015-CAEPIA-mldr.pdf (2.39 MB)

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)

2016-HAIS-Complexity.pdf (1.28 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)

2017-HAIS-CNNImbalance-compressed.pdf (1.62 MB)

{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)

2016-HAIS-RUMDR.pdf (1.3 MB)

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)

ReturnOK: El pasado de la computación personal, García, Lina, Ruano Ildefonso, Charte Francisco, Molina Andrés, and Balsas José R. , V Congreso Internacional de Patrimonio e Historia de la Ingeniería, 4, Las Palmas de Gran Canaria (Spain), p.1–19, (2010)

2010-CIPHI-Actas.pdf (1.16 MB)

Slicer: Feature Learning for Class Separability with Least-Squares Support Vector Machine Loss and COVID-19 Chest X-Ray Case Study, Charte, David, Sevillano-García Iván, Lucena-González María Jesús, Martín-Rodríguez José Luis, Charte Francisco, and Herrera Francisco , Hybrid Artificial Intelligent Systems, Cham, p.305–315, (2021)

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)

Usando Algoritmos de Descubrimiento de Subgrupos en R: El Paquete SDR, García-Vico, A.M., Charte Francisco, González P., Carmona C. J., and del Jesus M. J. , VII Simposio de Teoría y Aplicaciones de Minería de Datos, p.739-748, (2015)

2015 - TAMIDA-b.pdf (335 KB)

Journal Article

A Comprehensive and Didactic Review on Multilabel Learning Software Tools, Charte, Francisco , IEEE Access, 03/2020, Volume 8, p.50330-50354, (2020)

A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations, Charte, David, Charte Francisco, García S., and Herrera F. , Progress in Artificial Intelligence, 11, Volume 8, p.1-14, (2019)

2018-PRAI-NonStandard-Accepted.pdf (951.61 KB)

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)

An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges, Charte, David, Charte Francisco, del Jesus M. J., and Herrera F. , Neurocomputing, Volume 404, p.93-107, (2020)

1-s2.0-S092523122030624X-main.pdf (0 bytes)

Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications, M.Górriz, Juan, Ramírez Javier, Ortíz Andrés, Martínez-Murcia Francisco J., Segovia Fermín, Suckling John, Leming Matthew, Zhang Yu-Dong, Álvarez-Sánchez José Ramón, Bologna Guido, et al. , Neurocomputing, Volume 410, p.237-270, (2020)

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)

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)

E2PAMEA: A fast evolutionary algorithm for extracting fuzzy emerging patterns in big data environments, García-Vico, A.M., Charte Francisco, González P., Elizondo D., and Carmona C. J. , Neurocomputing, 11/2020, Volume 415, p.60-73, (2020)

1-s2.0-S0925231220311139-main.pdf (927.85 KB)

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)

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)

Is the average photon energy a unique characteristic of the spectral distribution of global irradiance?, Nofuentes, G, Gueymard CA, Aguilera J., Pérez-Godoy M.D., and Charte Francisco , Solar Energy, Volume 149, p.32–43, (2017)

2017-SolarEnergy-Photon-compressed.pdf (534.33 KB)

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)

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)

Reducing Data Complexity using Autoencoders with Class-informed Loss Functions, Charte, David, Charte Francisco, and Herrera Francisco , IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume In Press, (2021)

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)

ReturnOK, la Wiki sobre retroinformática, Cabrera, Lina García, Ruano Ildefonso, Charte Francisco, Aguilar Andrés Molina, and Almagro José Ramón Bal , Iniciación a la Investigación, Volume 5, p.1-5, (2010)

2010-IniciaInvUJA-ReturnOK.pdf (82.73 KB)

Ruta: implementations of neural autoencoders in R, Charte, David, Herrera F., and Charte Francisco , Knowledge-Based Systems, 06/2019, Volume 174, p.4-8, (2019)

1-s2.0-S0950705119300140-main.pdf (421.73 KB)

Smartdata: Data preprocessing to achieve smart data in R, Cordon, I., Luengo Julián, García Salvador, Herrera F., and Charte Francisco , Neurocomputing, 09/2019, Volume 360, p.1-13, (2019)

Subgroup Discovery with Evolutionary Fuzzy Systems in R: the SDEFSR Package, García-Vico, A.M., Charte Francisco, González P., Carmona C. J., and del Jesus M. J. , The R Journal, Volume 8, Issue 2, p.307-323, (2016)

2016-Garcia-RJournal.pdf (2.56 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)

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)

Working with Multilabel Datasets in R: The mldr Package, Charte, Francisco, and Charte David , The R Journal, Volume 7, Number 2, p.149–162, (2015)

2015-RJournal-mldr.pdf (1.16 MB)