Publicaciones
2020
Charte Ojeda, F. ., Rivera Rivas, A. J. ., Medina, J. ., & Espinilla, M. . (2020). El ecosistema de aprendizaje del estudiante universitario en la post-pandemia. Metodologías y herramientas. Enseñanza Y Aprendizaje De Ingeniería De Computadores. https://doi.org/10.30827/Digibug.64779
Charte Ojeda, F. ., Rivera Rivas, A. J. ., Martínez, F. ., & del Jesus, M. J. . (2020). EvoAAA: An evolutionary methodology for automated neural autoencoder architecture search. Integrated Computer-Aided Engineering, 27, 211-231. https://doi.org/10.3233/ICA-200619 (Original work published 2020)
García-Vico, Ángel M. ., Carmona del Jesus, C. J. ., González García, P. ., Seker, H. ., & del Jesus, M. J. . (2020). FEPDS: A Proposal for the Extraction of Fuzzy Emerging Patterns in Data Streams. IEEE Transactions on Fuzzy Systems, 28, 3193-3203. https://doi.org/10.1109/TFUZZ.2020.2992849 (Original work published 2020)
BES-2016-077738
González-Almagro, G. ., Rosales-Pérez, A. ., Luengo, J. ., Cano De Amo, J. R. . ., & García López, S. . (2020). Improving constrained clustering via decomposition-based multiobjective optimization with memetic elitism. GECCO ’20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 333-341. https://doi.org/10.1145/3377930.3390187 (Original work published 2020)
TIN2017-89517-P; PP2016.PRI.I.02.
Gonzalez, M. ., Cano De Amo, J. R. . ., & García López, S. . (2020). ProLSFEO-LDL: Prototype Selection and Label- Specific Feature Evolutionary Optimization for Label Distribution Learning. Applied Sciences, 10, 3089. https://doi.org/10.3390/app10093089
TIN2017-89517-P
Cano De Amo, J. R. . ., Luengo, J. ., & García López, S. . (2020). Similarity-based and Iterative Label Noise Filters for Monotonic Classification. Proceedings of the 53rd Hawaii International Conference on System Sciences, 1698-1706. https://doi.org/10.24251/HICSS.2020.210
TIN2017-89517-P; TEC2015-69496-R; BigDaP-TOOLS - Ayudas Fundación BBVA a Equipos de Investigación Científica 2016
Segura-Delgado, A. ., Gacto, M. J., Alcalá, R. ., & Alcala-Fdez, J. . (2020). Temporal association rule mining: An overview considering the time variable as an integral or implied component. WIREs Data Mining and Knowledge Discovery, 10. https://doi.org/10.1002/widm.1367 (Original work published 2020)
TIN2017-89517-P; P18-RT-2248
2019
García-Vico, Ángel M. ., González García, P. ., Carmona del Jesus, C. J. ., & del Jesus, M. J. . (2019). A Big Data Approach for the Extraction of Fuzzy Emerging Patterns. Cognitive Computation, 11, 400-417. https://doi.org/10.1007/s12559-018-9612-7 (Original work published 2019)
TIN2015-68454-R; BES-2016-077738
A First Approximation to the Effects of Classical Time Series Preprocessing Methods on LSTM Accuracy
Viedma, D. T., Rivera Rivas, A. J. ., Charte Ojeda, F. ., & del Jesus, M. J. . (2019). A First Approximation to the Effects of Classical Time Series Preprocessing Methods on LSTM Accuracy. 270-280. https://doi.org/10.1007/978-3-030-20521-8_23 (Original work published 2019)
TIN2015-68454-R
Charte, D. ", Charte Ojeda, F. ., del Jesus, M. J. ., & Herrera Triguero, F. . (2019). A Showcase of the Use of Autoencoders in Feature Learning Applications. 412-421. https://doi.org/10.1007/978-3-030-19651-6_40 (Original work published 2019)
TIN2015-68854-R; TIN2017-89517-P
Charte, D. ", Charte Ojeda, F. ., García López, S. ., & Herrera Triguero, F. . (2019). A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations. Progress in Artificial Intelligence, 8, 1-14. https://doi.org/10.1007/s13748-018-00167-7 (Original work published)
Martínez, F. ., Charte Ojeda, F. ., Rivera Rivas, A. J. ., & Frías Bustamante, M. del P. . (2019). Automatic Time Series Forecasting with GRNN: A Comparison with Other Models. 198-209. https://doi.org/10.1007/978-3-030-20521-8_17 (Original work published 2019)
TIN2015-68454-R
Charte Ojeda, F. ., Rivera Rivas, A. J. ., Martínez, F. ., & del Jesus, M. J. . (2019). Automating Autoencoder Architecture Configuration: An Evolutionary Approach. 339-349. https://doi.org/10.1007/978-3-030-19591-5_35 (Original work published 2019)
TIN2015-68454-R
Charte Ojeda, F. ., Rivera Rivas, A. J. ., del Jesus, M. J. ., & Herrera Triguero, F. . (2019). Dealing with difficult minority labels in imbalanced mutilabel data sets. Neurocomputing, 326, 39-53. https://doi.org/10.1016/j.neucom.2016.08.158
TIN2014-57251-P,TIN2015-68454-R,P11-TIC-7765
Charte Ojeda, F. ., & García, L. . (2019). El pasado de la computación personal. Historia de la microinformática (2a Edición). Editorial Universidad de Jaén.
Fernández Hilario, A. L. . ., del Jesus, M. J. ., Cordón García, Óscar ., Marcelloni, F. ., & Herrera Triguero, F. . (2019). Evolutionary Fuzzy Sistems for Explainable Artificial Intelligence: Why, When, What for, and Where to ?. IEEE Computational Intelligence, 1, 69-81. https://doi.org/10.1109/TFUZZ.2018.2814577
TIN2015-68454-R; TIN2015-67661-P; TIN2017-89517-P
Gacto, M. J., Soto-Hidalgo, J. M., Alcala-Fdez, J. ., & Alcalá, R. . (2019). Experimental Study on 164 Algorithms Available in Software Tools for Solving Standard Non-Linear Regression Problems. IEEE Access, 7, 108916-108939. https://doi.org/10.1109/ACCESS.2019.2933261 (Original work published 2019)
Cano De Amo, J. R. . ., Luengo, J. ., & García López, S. . (2019). Label noise filtering techniques to improve monotonic classification. Neurocomputing, 353, 83-95. https://doi.org/10.1016/j.neucom.2018.05.131 (Original work published 2019)
TIN2014-57251-P; TIN2017-89517-P; TEC2015-69496-R; BigDaP-TOOLS
Cano De Amo, J. R. . ., Gutiérrez, P. A., Krawczyk, B. ., Woźniak, M. ., & García López, S. . (2019). Monotonic classification: An overview on algorithms, performance measures and data sets. Neurocomputing, 341, 168-182. https://doi.org/10.1016/j.neucom.2019.02.024 (Original work published 2019)
TIN2017-89517-P; TIN2015-70308-REDT; TIN2014-54583-C2-1-R; TEC2015-69496-R
Charte Ojeda, F. ., Vico, A. ., Pérez Godoy, M. D. . ., & Rivera Rivas, A. J. . (2019). predtoolsTS: R package for streamlining time series forecasting. Progress in Artificial Intelligence, 8, 505-510. https://doi.org/10.1007/s13748-019-00193-z (Original work published 2019)
TIN2015-68854-R
Charte Ojeda, F. ., Rivera Rivas, A. J. ., del Jesus, M. J. ., & Herrera Triguero, F. . (2019). REMEDIAL-HwR: Tackling multilabel imbalance through label decoupling and data resampling hybridization. Neurocomputing, 326, 110-122. https://doi.org/10.1016/j.neucom.2017.01.118
TIN2014-57251-P,TIN2015-68454-R,P11-TIC-7765
Charte, D. ", Herrera Triguero, F. ., & Charte Ojeda, F. . (2019). Ruta: implementations of neural autoencoders in R. Knowledge-Based Systems, 174, 4-8. https://doi.org/- (Original work published 2019)
TIN2015-68854-R,BigDaP-TOOLS
Cordon, I. ., Luengo, J. ., García López, S. ., Herrera Triguero, F. ., & Charte Ojeda, F. . (2019). Smartdata: Data preprocessing to achieve smart data in R. Neurocomputing, 360, 1-13. https://doi.org/10.1016/j.neucom.2019.06.006 (Original work published 2019)
BigDaP-TOOLS - Ayudas Fundación BBVA a Equipos de Investigación Científica 2016
García-Vico, Ángel M. ., González García, P. ., Carmona del Jesus, C. J. ., & del Jesus, M. J. . (2019). Study on the use of different quality measures within a multi-objective evolutionary algorithm approach for emerging pattern mining in big data environments. Big Data Analytics, 4, 1. https://doi.org/10.1186/s41044-018-0038-8
TIN2015-68454-R; BES-2016-077738
Luna, J. M., Carmona del Jesus, C. J. ., García-Vico, Ángel M. ., del Jesus, M. J. ., & Ventura, S. . (2019). Subgroup Discovery on Multiple Instance Data. International Journal of Computational Intelligence Systems, 12, 1602-1612. https://doi.org/10.2991/ijcis.d.191213.001 (Original work published 2019)
TIN2017-83445-P; TIN2015-68454-R
Martínez, F. ., Frías Bustamante, M. del P. ., Charte Ojeda, F. ., & Rivera Rivas, A. J. . (2019). Time Series Forecasting with KNN in R: the tsfknn Package. The R Journal, 11, 229-242. https://doi.org/10.32614/RJ-2019-004 (Original work published 2019)
TIN2015-68854-R
2018
Rubio, F. J. . P., Charte Ojeda, F. ., Rivera Rivas, A. J. ., & del Jesus, M. J. . (2018). A First Approach to Face Dimensionality Reduction Through Denoising Autoencoders. 439-447. Madrid (Spain). https://doi.org/10.1007/978-3-030-03493-1_46 (Original work published)
TIN2015-68854-R; FPU16/00324
Cano De Amo, J. R. . ., & García López, S. . (2018). A First Attempt on Monotonic Training Set Selection (F. J. de C. Juez, J. R. Villar, E. A. de la Cal, álvaro . Herrero, H. . Quintián, J. A. Sáez, & E. . Corchado, Eds.). Cham: Springer International Publishing.
Fernández Hilario, A. L. . ., Carmona del Jesus, C. J. ., del Jesus, M. J. ., & Herrera Triguero, F. . (2018). A Pareto Based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets. Proc. Of the XVIII Conferencia De La Asociación Española Para La Inteligencia Artificial (XVIII CAEPIA), 1316-1317.
TIN2015-68454-R, TIN2017-89517-P
Charte, D. ", Charte Ojeda, F. ., García López, S. ., del Jesus, M. J. ., & Herrera Triguero, F. . (2018). A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines. 949-950. Granada (Spain). (Original work published)
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Charte, D. ", Charte Ojeda, F. ., García López, S. ., & Herrera Triguero, F. . (2018). A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations. Progress in Artificial Intelligence. https://doi.org/10.1007/s13748-018-00167-7 (Original work published 2024)
TIN2017-89517-P; TIN2015-68454-R
A Unifying Analysis for the Supervised Descriptive Rule Discovery via the Weighted Relative Accuracy
Carmona del Jesus, C. J. ., del Jesus, M. J. ., & Herrera Triguero, F. . (2018). A Unifying Analysis for the Supervised Descriptive Rule Discovery via the Weighted Relative Accuracy. Knowledge-Based Systems, 139, 89-100. https://doi.org/10.1016/j.knosys.2017.10.015
TIN2014-915 57251-P, TIN2015-68454-R
Rubio, F. J. . P., Charte Ojeda, F. ., Rivera Rivas, A. J. ., & del Jesus, M. J. . (2018). AEkNN: An AutoEncoder kNN-Based Classifier With Built-in Dimensionality Reduction. International Journal of Computational Intelligence Systems, 12, 436-452. https://doi.org/10.2991/ijcis.2019.0025 (Original work published 2018)
TIN2015-68854-R; FPU16/00324
Trujillo, D. ., Rivera Rivas, A. J. ., Charte Ojeda, F. ., & del Jesus, M. J. . (2018). An Approximation to Deep Learning Touristic-Related Time Series Forecasting. 448-456. Madrid (Spain). https://doi.org/10.1007/978-3-030-03493-1_47 (Original work published)
TIN2015-68854-R
García-Vico, Ángel M. ., Carmona del Jesus, C. J. ., Martín, D. ., García-Borroto, M. ., & del Jesus, M. J. . (2018). An Overview of Emerging Pattern Mining in Supervised Descriptive Rule Discovery: Taxonomy, Empirical Study, Trends and Prospects. WIREs Data Mining and Knowledge Discovery, 8. https://doi.org/10.1002/widm.1231
TIN2015-68454-R;BES-2016-077738
Rubio, F. J. . P., Rivera Rivas, A. J. ., Charte Ojeda, F. ., & Díaz, M. J. del J. (2018). Análisis del impacto de datos desbalanceados en el rendimiento predictivo de redes neuronales convolucionales. 1213-1218. Granada (Spain). (Original work published)
TIN2015-68454-R; FPU16/00324
Puentes, F. ., Pérez Godoy, M. D. . ., González García, P. ., & del Jesus, M. J. . (2018). Análisis preliminar de marcos tecnológicos en data stream. 1117-1122. Granada (España). (Original work published)
Carmona del Jesus, C. J. ., del Jesus, M. J. ., & Herrera Triguero, F. . (2018). Atipicidad: Medida de calidad clave dentro del descubrimiento de reglas descriptivas supervisadas. 827-828.
TIN2014-916 57251-P, TIN2015-68454-R
Mantas, C. J., Abellán, J. ., Castellano, J. G., Cano De Amo, J. R. . ., & Moral, S. . (2018). Credal C4.5 with Refinement of~Parameters (J. . Medina, M. . Ojeda-Aciego, J. L. Verdegay, I. . Perfilieva, B. . Bouchon-Meunier, & R. R. Yager, Eds.). Cham: Springer International Publishing.
Martínez, F. ., Frías Bustamante, M. del P. ., Pérez Godoy, M. D. . ., & Rivera Rivas, A. J. . (2018). Dealing with seasonality by narrowing the training set in time series forecasting with kNN. Expert Systems With Applications, 103, 38-48. https://doi.org/10.1016/j.eswa.2018.03.005
Alcalá, R. ., Gacto, M. J., & Alcala-Fdez, J. . (2018). Evolutionary Data Mining and Applications: A Revision on the Most Cited Papers from the Last 10 Years (2007-2017). WIREs Data Mining and Knowledge Discover, 8, 1-17. https://doi.org/10.1002/widm.1239
TIN2014-57251-P, TIN2015-68454-R, P11-TIC-7765
González García, P. ., García-Vico, Ángel M. ., Carmona del Jesus, C. J. ., & del Jesus, M. J. . (2018). Improvement of subgroup descriptions in noisy data by detecting exceptions. Progress in Artificial Intelligence, 7, 55-64. https://doi.org/10.1007/s13748-017-0131-7
TIN2015-68454-R
García-Vico, Ángel M. . (2018). Modelos descriptivos basados en aprendizaje supervisado para el tratamiento de grandes volúmenes de datos y flujos continuos de datos. 1402-1407.
TIN2015-68454-R;BES-2016-077738
García-Vico, Ángel M. ., Carmona del Jesus, C. J. ., González García, P. ., & del Jesus, M. J. . (2018). MOEA-EFEP: Multi-Objective Evolutionary Algorithm for Extracting Fuzzy Emerging Patterns. IEEE Transaction on Fuzzy Systems, 26, 2861-2872. https://doi.org/10.1109/TFUZZ.2018.2814577
TIN2015-68454-R;BES-2016-077738
García-Vico, Ángel M. ., Carmona del Jesus, C. J. ., González García, P. ., & del Jesus, M. J. . (2018). MOEA-EFEP: Un algoritmo evolutivo multi-objetivo para la extracción de patrones emergentes difusos. 671-672.
TIN2015-68454-R;BES-2016-077738
Rivera Rivas, A. J. ., Charte Ojeda, F. ., Espinilla, M. ., & Pérez Godoy, M. D. . . (2018). Nuevas arquitecturas hardware de procesamiento de alto rendimiento para aprendizaje profundo. Enseñanza Y Aprendizaje De ingeniería De Computadores. Revista De Experiencias Docentes En ingeniería De Computadores, 8, 67-83.
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Luna, J. ., Reyes, O. ., del Jesus, M. J. ., & Ventura, S. . (2018). Reglas de asociación en datos multi-instancia mediante programación genética gramatical. 815-820. Granada. (Original work published)
Charte Ojeda, F. ., Rivera Rivas, A. J. ., Charte, D. ", del Jesus, M. J. ., & Herrera Triguero, F. . (2018). Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository. Neurocomputing, 289, 68-85. https://doi.org/10.1016/j.neucom.2018.02.011
TIN2014-57251-P,TIN2015-68454-R,BigDaP-TOOLS
Viedma, D. T., Rivera Rivas, A. J. ., Charte Ojeda, F. ., & Díaz, M. J. del J. (2018). Una primera aproximación a la predicción de variables turísticas con Deep Learning. 939-943. Granada (Spain). (Original work published)
TIN2015-68454-R
García-Vico, Ángel M. ., Carmona del Jesus, C. J. ., González García, P. ., & del Jesus, M. J. . (2018). Una primera aproximación para la extracción de patrones emergentes en flujos continuos de datos. 1093-1098. Mejor trabajo del II Workshop en Big Data y Análisis de Datos Escalable - BigDADE 2018.
TIN2015-68454-R;BES-2016-077738