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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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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)
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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon Pulgar2017_Chapter_OnTheImpactOfImbalancedDataInC.pdf (634.69 KB)
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) PDF icon 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) PDF icon RUMDR.pdf (294.87 KB)
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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 2018-PRAI-NonStandard-Accepted.pdf (951.61 KB)
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, Nov, (2018)
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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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)
Strategies for time series forecasting with generalized regression neural networks, Martínez, Francisco, Charte Francisco, Frías María Pilar, and Martínez-Rodríguez Ana María , Neurocomputing, Volume 491, p.509-521, (2022)
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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 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) PDF icon 2015-RJournal-mldr.pdf (1.16 MB)