Publicaciones
2018
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
2017
García-Vico, Ángel M. ., González García, P. ., del Jesus, M. J. ., & Carmona del Jesus, C. J. . (2017). A First Approach to Handle Emergining Patterns Mining on Big Data Problems: The EvAEFP-Spark Algorithm. 1-6.
Prati, R. C., Charte Ojeda, F. ., & Herrera Triguero, F. . (2017). A first approach towards a fuzzy decision tree for multilabel classification. 1-6. Naples (Italy). https://doi.org/10.1109/FUZZ-IEEE.2017.8015521 (Original work published)
TIN2014- 57251-P,P11-TIC-7765
Martínez, F. ., Frías Bustamante, M. del P. ., Pérez Godoy, M. D. . ., & Rivera Rivas, A. J. . (2017). A methodology for applying k-nearest neighbor to time series forecasting. Artificial Intelligence Review. https://doi.org/10.1007/s10462-017-9593-z (Original work published 2024)
Fernández Hilario, A. L. . ., Carmona del Jesus, C. J. ., del Jesus, M. J. ., & Herrera Triguero, F. . (2017). A Pareto Based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets. International Journal of Neural Systems, 27, 1-17. https://doi.org/10.1142/S0129065717500289
TIN2014-57251-P, TIN2015-68454-R, P11-TIC-7765, UJA2014/06/15
Martínez, F. ., Frías, M. ., Charte Ojeda, F. ., & Rivera Rivas, A. J. . (2017). A specialized lazy learner for time series forecasting. 1397-1403. Costa Ballena, Rota, Cáadiz (Spain). (Original work published)
TIN2015-68854-R
Rivera Rivas, A. J. ., Charte Ojeda, F. ., Rubio, F. J. . P., & del Jesus, M. J. . (2017). A Transformation Approach Towards Big Data Multilabel Decision Trees. 73-84. Cádiz (Spain). https://doi.org/10.1007/978-3-319-59153-7_7 (Original work published)
TIN2015-68454-R
Couso, I. ., & Sánchez, L. . (2017). Additive similarity and dissimilarity measures. Fuzzy Sets and Systems, 322, 35-53. https://doi.org/10.1016/j.fss.2016.12.013
Theme: Preference and Similarity
García-Vico, Ángel M. ., Carmona del Jesus, C. J. ., & del Jesus, M. J. . (2017). Análisis de Diferentes Tipos de Reglas en Sistemas Difusos Evolutivos para Minería de Patrones Emergentes. 876-885.
TIN2015-68454-R
Jarwar, M. A., Abbasi, R. A., Mushtaq, M. ., Maqbool, O. ., Aljohani, N. R., Daud, A. ., … Chong, I. . (2017). CommuniMents: A Framework for Detecting Community Based Sentiments for Events. International Journal on Semantic Web and Information Systems, 13, 87-108.
Charte Ojeda, F. ., Romero, I. ., Pérez Godoy, M. D. . ., Rivera Rivas, A. J. ., & Castro, E. . (2017). Comparative analysis of data mining and response surface methodology predictive models for enzymatic hydrolysis of pretreated olive tree biomass. Computers \& Chemical Engineering, 101, 23-30. https://doi.org/10.1016/j.compchemeng.2017.02.008
ENE2014-60090-C2-2-R,TIN2015-68454-R
Frías, M. ., Ivanov, A. ., Leonenko, N. ., Martínez, F. ., & Ruiz-Medina, M. D. (2017). Detecting hidden periodicities for models with cyclical errors. Statistics and Its Interface, 10, 107-118. https://doi.org/10.4310/SII.2017.v10.n1.a10 (Original work published)
Charte Ojeda, F. ., Rueda, A. J., Espinilla, M. ., & Rivera Rivas, A. J. . (2017). Evolución tecnológica del hardware de vídeo y las GPU en los ordenadores personales. Enseñanza Y Aprendizaje De ingeniería De Computadores. Revista De Experiencias Docentes En ingeniería De Computadores, 7, 111-128.
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García-Vico, Ángel M. ., González García, P. ., Carmona del Jesus, C. J. ., & del Jesus, M. J. . (2017). Impact of the Type of Rule in Fuzzy Emerging Pattern Mining on a Big Data Approach. Presentado en.
TIN2015-68454-R
Nofuentes, G. ., Gueymard, C. ., García, J. J. . A., Pérez Godoy, M. D. . ., & Charte Ojeda, F. . (2017). Is the average photon energy a unique characteristic of the spectral distribution of global irradiance?. Solar Energy, 149, 32-43. https://doi.org/10.1016/j.solener.2017.03.086
ENE2008-05098-ALT
Triguero, I. ., Gonzalez, S. ., Moyano, J. ., García López, S. ., Alcala-Fdez, J. ., Luengo, J. ., … PRESS., A. . (2017). KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining. International Journal of Computational Intelligence Systems, 10, 1238-1249.
Rubio, F. J. . P., Rivera Rivas, A. J. ., Pérez Godoy, M. D. . ., González García, P. ., Carmona del Jesus, C. J. ., & del Jesus, M. J. . (2017). MEFASD-BD: Multi-Objective Evolutionary Algorithm for Subgroup Discovery in Big Data Environments - A MapReduce Solution. Knowledge-Based Systems, 117, 70-78. https://doi.org/10.1016/j.knosys.2016.08.021
TIN2015-68454-R
Sánchez, O. ., Moyano, J. M., Sánchez, L. ., & Alcala-Fdez, J. . (2017). Mining association rules in R using the package RKEEL. 1-6. https://doi.org/10.1109/FUZZ-IEEE.2017.8015572 (Original work published 2024)
Charte Ojeda, F. ., Romero, I. ., Rivera Rivas, A. J. ., & Castro, E. . (2017). Modeling the Transformation of Olive Tree Biomass into Bioethanol with Reg-CO2RBFN. 733-744. Cádiz (Spain). https://doi.org/10.1007/978-3-319-59153-7_63 (Original work published)
TIN2015-68454-R
García, J. ., Fardoun, H. M., Alghazzawi, D. M., Cano De Amo, J. R. . ., & García López, S. . (2017). MoNGEL: monotonic nested generalized exemplar learning. Pattern Analysis and Applications, 20, 441-452. https://doi.org/10.1007/s10044-015-0506-y (Original work published 2024)
Rubio, F. J. . P., Rivera Rivas, A. J. ., Charte Ojeda, F. ., & del Jesus, M. J. . (2017). On the Impact of Imbalanced Data in Convolutional Neural Networks Performance. 220-232. La Rioja (Spain). https://doi.org/10.1007/978-3-319-59650-1_19 (Original work published)
TIN2015-68454-R
Cano De Amo, J. R. . ., Aljohani, N. R., Abbasi, R. A., Alowidbi, J. S., & García López, S. . (2017). Prototype selection to improve monotonic nearest neighbor. Engineering Applications of Artificial Intelligence, 60, 128-135. https://doi.org/10.1016/j.engappai.2017.02.006
Rivas, V. ., Parras Gutiérrez, E. . ., Merelo, J. ., Arenas, M. ., & Garcia-Fernandez, P. . (2017). Time series forecasting using evolutionary neural nets implemented in a volunteer computing system. Intelligent Systems in Accounting, Finance and Management, 24, 87-95. https://doi.org/10.1002/isaf.1409
Cano De Amo, J. R. . ., & García López, S. . (2017). Training set selection for monotonic ordinal classification. Data \& Knowledge Engineering, 112, 94-105. https://doi.org/10.1016/j.datak.2017.10.003
Echevarría, Y. ., Couso, I. ., Anseán, D. ., Blanco, C. ., & Sánchez, L. . (2017). Using fuzzy preference orderings in theta-dominance with application to health monitoring of Li-ion batteries. Journal of Multiple-Valued Logic and Soft Computing.
Charte Ojeda, F. ., Espinilla, M. ., Rivera Rivas, A. J. ., & Rubio, F. J. . P. (2017). Uso de dispositivos FPGA como apoyo a la enseñanza de asignaturas de arquitectura de computadores. Enseñanza Y Aprendizaje De ingeniería De Computadores. Revista De Experiencias Docentes En ingeniería De Computadores, 7, 37-52.
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2016
Carmona del Jesus, C. J. ., & Elizondo, D. . (2016). Supervised Descriptive Rule Discovery: A Survey of the State-of-the-Art (pp. 1-11).
Ruiz-Rodado, V. ., Carmona del Jesus, C. J. ., Grootveld, M. ., & Elizondo, D. . (2016). New Developments in 1H NMR-linked Metabolomics: Identification of New Biomarkers for the Metabolomic Classification of Niemann-Pick Disease, Type C1, and its Response to Treatment. Leicester School of Pharmacy, De Montfort University, Leicester, United Kingdom.
Fernández Hilario, A. L. . ., Carmona del Jesus, C. J. ., del Jesus, M. J. ., & Herrera Triguero, F. . (2016). A View on Fuzzy Systems for Big Data: Progress and Opportunities. International Journal of Computational Intelligence Systems, 9, 69-80.
TIN2014-57251-P, P11-TIC-7765, UJA2014/06/15
Palacios, A. M., Sánchez, L. ., Couso, I. ., & Destercke, S. . (2016). An extension of the FURIA classification algorithm to low quality data through fuzzy rankings and its application to the early diagnosis of dyslexia. Neurocomputing, 176, 60-71. https://doi.org/10.1016/j.neucom.2014.11.088
Recent Advancements in Hybrid Artificial Intelligence Systems and its Application to Real-World Problems
Charte, D. ", Charte Ojeda, F. ., & Herrera Triguero, F. . (2016). Análisis visual de técnicas no supervisadas de deep learning con el paquete dlvisR. 895-904. Salamanca (Spain). (Original work published)
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García-Vico, Ángel M. ., Montes, J. ., García, J. J. . A., Carmona del Jesus, C. J. ., & del Jesus, M. J. . (2016). Analysing Concentrating Photovoltaics Technology through the use of Emerging Pattern Mining. Presentado en.
ENE2009-08302, P09-TEP-5045, TIN2015-68454-R
Echevarría, Y. ., Sánchez, L. ., & Blanco, C. . (2016). Assessment of Multi-Objective Optimization Algorithms for Parametric Identification of a Li-Ion Battery Model (F. . Martínez-álvarez, A. . Troncoso, H. . Quintián, & E. . Corchado, Eds.). Cham: Springer International Publishing.
Sánchez, L. ., Otero, J. ., Couso, I. ., & Blanco, C. . (2016). Battery diagnosis for electrical vehicles through semi-physical fuzzy models. 416-423. https://doi.org/10.1109/FUZZ-IEEE.2016.7737717 (Original work published 2024)
Martínez, F. ., Pérez Godoy, M. D. . ., Charte Ojeda, F. ., & del Jesus, M. J. . (2016). Combining simple exponential smoothing models for time series forecasting. 635-644. Granada (Spain). (Original work published)
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Carmona del Jesus, C. J. ., Rubio, F. J. . P., Rivera Rivas, A. J. ., del Jesus, M. J. ., & García, J. J. . A. (2016). Estimating the Maximum Power Delivered by Concentrating Photovoltaics Technology Through Atmospheric Conditions Using a Differential Evolution Approach. 273-282. Sevilla (Spain): Springer. (Original work published 2024)
ENE2009-08302, P09-TEP-5045, TIN2015-68454-R
Charte Ojeda, F. ., Rivera Rivas, A. J. ., Rubio, F. J. . P., & Díaz, M. J. del J. (2016). Explotación de la potencia de procesamiento mediante paralelismo: un recorrido histórico hasta la GPGPU. Enseñanza Y Aprendizaje De ingeniería De Computadores. Revista De Experiencias Docentes En ingeniería De Computadores, 6, 19-33.
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Otero, J. ., Junco, L. ., Suárez, R. ., Palacios, A. M., Couso, I. ., & Sánchez, L. . (2016). Finding informative code metrics under uncertainty for predicting the pass rate of online courses. Information Sciences, 373, 42-56. https://doi.org/10.1016/j.ins.2016.08.090
Espinilla, M. ., Santamaría, J. ., & Rivera Rivas, A. J. . (2016). Gamificación en procesos de autoentrenamiento y autoevaluación. Experiencia en la asignatura de Arquitectura de Computadores. 6, 55-65.
Echevarría, Y. ., Sánchez, L. ., & Blanco, C. . (2016). Genetic Fuzzy Modelling of Li-Ion Batteries Through a Combination of Theta-DEA and Knowledge-Based Preference Ordering (O. . Luaces, J. A. Gámez, E. . Barrenechea, A. . Troncoso, M. . Galar, H. . Quintián, & E. . Corchado, Eds.). Cham: Springer International Publishing.
Cocaña-Fernández, A. ., Sánchez, L. ., & Ranilla, J. . (2016). Improving the Eco-Efficiency of High Performance Computing Clusters Using EECluster. Energies, 9, 197. https://doi.org/10.3390/en9030197 (Original work published)
Cocaña-Fernández, A. ., Sánchez, L. ., & Ranilla, J. . (2016). Leveraging a predictive model of the workload for intelligent slot allocation schemes in energy-efficient HPC clusters. Engineering Applications of Artificial Intelligence, 48, 95-105. https://doi.org/10.1016/j.engappai.2015.10.003
Couso, I. ., & Sánchez, L. . (2016). Machine learning models, epistemic set-valued data and generalized loss functions: An encompassing approach. Information Sciences, 358-359, 129-150. https://doi.org/10.1016/j.ins.2016.04.016
García-Vico, Ángel M. ., Carmona del Jesus, C. J. ., González García, P. ., & del Jesus, M. J. . (2016). Minería de Patrones Emergentes: Una oportunidad para la extracción evolutiva de conocimiento. Presentado en.
TIN2015-68454-R
Charte Ojeda, F. ., Rivera Rivas, A. J. ., del Jesus, M. J. ., & Herrera Triguero, F. . (2016). MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation. XVII Conferencia De La Asociación Española Para La Inteligencia Artificial (CAEPIA 2016), 821-822. Salamanca (Spain). (Original work published)
TIN2012-33856,TIN2011-28488,P10-TIC-6858,P11-TIC-7765
Herrera Triguero, F. ., Charte Ojeda, F. ., Rivera Rivas, A. J. ., & del Jesus, M. J. . (2016). Multilabel Classification: Problem Analysis, Metrics and Techniques. Springer. https://doi.org/10.1007/978-3-319-41111-8
Charte Ojeda, F. ., Rivera Rivas, A. J. ., del Jesus, M. J. ., & Herrera Triguero, F. . (2016). On the Impact of Dataset Complexity and Sampling Strategy in Multilabel Classifiers Performance. 500-511. Seville (Spain). https://doi.org/10.1007/978-3-319-32034-2_42 (Original work published)
TIN2014-57251-P,TIN2012-33856,P10-TIC-06858,P11-TIC-7765
Moyano, J. ., & Sánchez, L. . (2016). RKEEL: Using KEEL in R code. 257-264. https://doi.org/10.1109/FUZZ-IEEE.2016.7737695 (Original work published)
García-Vico, Ángel M. ., Charte Ojeda, F. ., González García, P. ., Carmona del Jesus, C. J. ., & del Jesus, M. J. . (2016). Subgroup Discovery with Evolutionary Fuzzy Systems in R: the SDEFSR Package. The R Journal, 8, 307-323.
TIN2015-68854-R
Luengo, J. ., García-Vico, Ángel M. ., Pérez Godoy, M. D. . ., & Carmona del Jesus, C. J. . (2016). The Influence of Noise on the Evolutionary Fuzzy Systems for Subgroup Discovery. Soft Computing, 20, 4313-4330. https://doi.org/10.1007/s00500-016-2300-1
TIN2015-68454-R, TIN2014-57251-P, P11-TIC-7765, P12-TIC-2958