SCI2S Publications (J.M. Benítez)

Number of Results: 123

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In Press (3)

• [2597] F.J. Baldán, J.M. Benítez. Distributed FastShapelet Transform: a Big Data time series classification algorithm. Information Sciences, Accepted, in press. doi: 10.1016/j.ins.2018.10.028
  COMPLEMENTARY MATERIAL to the paper: datasets, experimental results and source codes
  
  
• [2171] S. Ramírez-Gallego, H. Mouriño-Talín, D. Martínez-Rego, V. Bolón-Canedo, J.M. Benítez, A. Alonso-Betanzos, F. Herrera. An Information Theory-Based Feature Selection Framework for Big Data under Apache Spark. IEEE Transactions on Systems, Man, and Cybernetics: Systems (2017), in press. doi: 10.1109/TSMC.2017.2670926
  
• [2254] P.D. Gutiérrez, M. Lastra, J.M. Benítez, F. Herrera. SMOTE-GPU: Big Data preprocessing on commodity hardware for imbalanced classification. Progress in Artificial Intelligence, in press. doi: 10.1007/s13748-017-0128-2
  

2018 (3)

• [2309] M. Gonzalez, C. Bergmeir, I. Triguero, Y. Rodriguez, J.M. Benitez. Self-labeling techniques for semi-supervised time series classification: an empirical study. Knowledge and Information Systems, 55 (2), 493-528. doi: 10.1007/s10115-017-1090-9
  
• [2319] S. Ramírez-Gallego, S. García, J.M. Benítez, F. Herrera. A distributed evolutionary multivariate discretizer for Big Data processing on Apache Spark. Swarm and Evolutionary Computation 38 (2018) 240-250. doi: 10.1016/j.swevo.2017.08.005
  
• [2364] D. Peralta, I. Triguero, S. García, Y. Saeys, J.M. Benítez, F. Herrera. On the use of convolutional neural networks for robust classiffication of multiple fingerprint captures. International Journal of Intelligent Systems 33:1 (2018) 213–230. doi: 10.1002/int.21948
  

2017 (5)

• [2096] S. Ramírez-Gallego, I. Lastra, D. Martínez-Rego, V. Bolón-Canedo, J.M. Benítez, F. Herrera, A. Alonso-Betanzos. Fast-mRMR: Fast minimum Redundancy Maximum Relevance algorithm for high dimensional big data. International Journal of Intelligent Systems 32:2 (2017) 134-152. doi: 10.1002/int.21833
  
• [2151] D. Peralta, I. Triguero, S. García, Y. Saeys, J.M. Benítez, F. Herrera. Distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection. Knowledge-Based Systems 126 (2017) 91-103. doi: 10.1016/j.knosys.2017.03.014
  
• [2154] D. Peralta, S. García, J.M. Benítez, F. Herrera. Minutiae-Based Fingerprint Matching Decomposition: Methodology for Big Data Frameworks. Information Sciences 408 (2017) 198-212. doi: 10.1016/j.ins.2017.05.001
  
• [2170] S. Ramírez-Gallego, B. Krawczyk, S. García, M. Wozniak, J.M. Benítez, F. Herrera. Nearest Neighbor Classification for High-Speed Big Data Streams Using Spark. IEEE Transactions on Systems, Man, and Cybernetics: Systems 47:10 (2017) 2727-2739. doi: 10.1109/TSMC.2017.2700889
  
• [2595] S. Ramírez-Gallego, B. Krawczyk, S. García, M. Wozniak, J.M. Benítez, F. Herrera. Fast Case-Based Reasoning for Large-Scale Streaming Classification. 8th International Conference of Pattern Recognition Systems (ICPRS 2017), Madrid (Spain), July 11-13, 2017. doi: 10.1049/cp.2017.0150
  

2016 (7)

• [1896] S. Ramírez-Gallego, S. García, J.M. Benítez, F. Herrera. Multivariate Discretization Based on Evolutionary Cut Points Selection for Classification. IEEE Transaction on Cybernetics 46:3 (2016) 595-608. doi: 10.1109/TCYB.2015.2410143
  
• [1949] M. González, C. Bergmeir, I. Triguero, Y. Rodríguez, J.M. Benítez. On the Stopping Criteria for k-Nearest Neighbor in Positive Unlabeled Time Series Classification Problems. Information Sciences 328 (2016) 42-59. doi: 10.1016/j.ins.2015.07.061
  
• [2075] D. Peralta, I. Triguero, S. García, F. Herrera, J.M. Benítez. DPD-DFF: A Dual Phase Distributed Scheme with Double Fingerprint Fusion for Fast and Accurate Identification in Large Databases. Information Fusion 32 (2016) 40–51. doi: 10.1016/j.inffus.2016.03.002
  
• [2162] S. García, S. Ramírez-Gallego, J. Luengo, J.M. Benítez, F. Herrera. Big data preprocessing: methods and prospects. Big Data Analytics 1:9 (2016). doi: 10.1186/s41044-016-0014-0
  
• [2178] S. Ramírez-Gallego, S. García, J.M. Benítez, F. Herrera. Discretización Multivariada basada en Selección de Puntos Evolutiva para Clasificación. XVII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA), XI Symposio de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB), September 14-16, 2016.
  
• [2252] P.D. Gutiérrez, M. Lastra, J. Bacardit, J.M. Benítez, F. Herrera. GPU-SME-kNN: Scalable and memory efficient kNN and lazy learning using GPUs. Information Sciences 373, 165-182. doi: 10.1016/j.ins.2016.08.089
  COMPLEMENTARY MATERIAL to the paper: experimental results and source codes
  
  
• [2353] C. Bergmeir, D. Molina, J.M. Benitez. Memetic algorithms with local search chains in R: The Rmalschains package. Journal of Statistical Software. 75:1 (2016) 1-33.. doi: 10.18637/jss.v075.i04
  R source code of the package, replication code from the manuscript
  
  

2015 (15)

• [1734] V. López, S. Río, J.M. Benítez, F. Herrera. Cost-Sensitive Linguistic Fuzzy Rule Based Classification Systems under the MapReduce Framework for Imbalanced Big Data. Fuzzy Sets and Systems 258 (2015) 5-38. doi: 10.1016/j.fss.2014.01.015
  
• [1822] M. Lastra, D. Molina, J.M. Benítez. A high performance memetic algorithm for extremely high-dimensional problems. Information Sciences, 293 (2015) 35-58. doi: 10.1016/j.ins.2014.09.018
  
• [1860] S. Río, V. López, J.M. Benítez, F. Herrera. A MapReduce Approach to Address Big Data Classification Problems Based on the Fusion of Linguistic Fuzzy Rules. International Journal of Computational Intelligence Systems 8:3 (2015) 422-437. doi: 10.1080/18756891.2015.1017377
  
• [1890] M. Galar, J. Derrac, D. Peralta, I. Triguero, D. Paternain, C. Lopez-Molina, S. García, J.M. Benítez, M. Pagola, E. Barrenechea, H. Bustince, F. Herrera. A Survey of Fingerprint Classification Part I: Taxonomies on Feature Extraction Methods and Learning Models. Knowledge-Based Systems 81 (2015) 76-97. doi: 10.1016/j.knosys.2015.02.008
  
• [1891] M. Galar, J. Derrac, D. Peralta, I. Triguero, D. Paternain, C. Lopez-Molina, S. García, J.M. Benítez, M. Pagola, E. Barrenechea, H. Bustince, F. Herrera. A Survey of Fingerprint Classification Part II: Experimental Analysis and Ensemble Proposal. Knowledge-Based Systems 81 (2015) 98-116. doi: 10.1016/j.knosys.2015.02.015
  COMPLEMENTARY MATERIAL to the paper: datasets, experimental results and source codes
  
  
• [1926] I. Triguero, S. Río, V. López, J. Bacardit, J.M. Benítez, F. Herrera. ROSEFW-RF: The winner algorithm for the ECBDL'14 Big Data Competition: An extremely imbalanced big data bioinformatics problem. Knowledge-Based Systems 87 (2015) 69-79. doi: 10.1016/j.knosys.2015.05.027
  
• [1929] L. Septem, C. Bergmeir, F. Herrera, J.M. Benítez. frbs: Fuzzy Rule-Based Systems for Classification and Regression in R. Statistical Software 65:6 (2015). doi: 10.18637/jss.v065.i06
  
• [1939] D. Peralta, S. Río, S. Ramírez-Gallego, I. Triguero, J.M. Benítez, F. Herrera. Evolutionary Feature Selection for Big Data Classification: A MapReduce Approach. Mathematical Problems in Engineering, vol. 2015, Article ID 246139 (2015) 11 pages. doi: 10.1155/2015/246139
  
• [1952] S. Río, J.M. Benítez, F. Herrera. Analysis of Data Preprocessing Increasing the Oversampling Ratio for Extremely Imbalanced Big Data Classification. The 9th IEEE International Conference on Big Data Science and Engineering(IEEE BigDataSE-15), Helsinki, Finland, 20-22 August, 2015.
  
• [1989] D. Peralta, I. Triguero, Y. Saeys, S. García, J.M. Benítez, F. Herrera. Clasificación Jerárquica de Huellas Dactilares con Selección de Características. VII Symposium of Theory and Applications of Data Mining (TAMIDA), CAEPIA 2015, Albacete (España), pp. 831-840, 09-12 Noviembre 2015.
  
• [1988] S. Río, J.M. Benítez, F. Herrera. Preprocesamiento de Datos mediante el Incremento de la Tasa de Sobremuestreo para Problemas de Big Data Extremadamente Desbalanceados. XVI Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2015), Albacete (Spain), 10 pages, 9-12 November, 2015.
  
• [2056] M. Lastra, J. Carabaño, P.D. Gutiérrez, J.M. Benítez, F. Herrera. Fast fingerprint identification using GPUs. Information Sciences. 301 (2015) 195-214. doi: doi:10.1016/j.ins.2014.12.052
  
• [2063] S. Ramírez-Gallego, S. Garcia, H. Mourino-Talin, D. Martinez-Rego, V. Bolon-Canedo, A. Alonso-Betanzos, J.M. Benítez, F. Herrera. Distributed Entropy Minimization Discretizer for Big Data Analysis under Apache Spark. 9th International Conference on Big Data Science and Engineering (IEEE BigDataSE-15), Helsinki (Finland), 33-40, August 20-22, 2015. doi: 10.1109/Trustcom.2015.559
  
• [2168] S. Ramírez-Gallego, S. García, J.M. Benítez, F. Herrera. A Wrapper Evolutionary Approach for Supervised Multivariate Discretization: A Case Study on Decision Trees. A Wrapper Evolutionary Approach for Supervised Multivariate Discretization: A Case Study on Decision Trees. In: Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. doi: 10.1007/978-3-319-26227-7_5
  
• [2255] P.D. Gutiérrez, M. Lastra, J. Bacardit, J.M. Benítez, F. Herrera. GPU-SME-kNN: kNN escalable y eficiente en memoria utilizando GPU. XVI Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2015), Albacete (Spain), pp. 959-968, 09-12 November, 2015.
  

2014 (10)

• [1670] D. Peralta, I. Triguero, R. Sanchez-Reillo, F. Herrera, J.M. Benítez. Fast Fingerprint Identification for Large Databases. Pattern Recognition 47:2 (2014) 588–602. doi: 10.1016/j.patcog.2013.08.002
  COMPLEMENTARY MATERIAL to the paper: datasets, experimental results and source codes
  
  
• [1742] S. Río, V. López, J.M. Benítez, F. Herrera. On the use of MapReduce for Imbalanced Big Data using Random Forest. Information Sciences 285 (2014) 112-137. doi: 10.1016/j.ins.2014.03.043
  COMPLEMENTARY MATERIAL to the paper
  
  
• [1703] P.D. Gutiérrez, M. Lastra, F. Herrera, J.M. Benítez. A high performance fingerprint matching system for large databases based on GPU. IEEE Transactions on Information Forensics and Security 9:1 (2014) 62-71. doi: 10.1109/TIFS.2013.2291220
  
• [1721] C. Bergmeir, M. Costantini, J.M. Benítez. On the usefulness of cross-validation for directional forecast evaluation. Computational Statistics and Data Analysis 76 (2014) 132-143. doi: 10.1016/j.csda.2014.02.001
  
• [1740] D. Peralta, M. Galar, I. Triguero, O. Miguel-Hurtado, J.M. Benítez, F. Herrera. Minutiae Filtering to Improve Both Efficacy and Efficiency of Fingerprint Matching Algorithms. Engineering Applications of Artificial Intelligence, 32 (2014) 37-53. doi: 10.1016/j.engappai.2014.02.016
  
• [1772] V. Bolón-Canedo, N. Sánchez-Maroño, A. Alonso-Betanzos, J.M. Benítez, F. Herrera. A review of microarray datasets and applied feature selection methods. Information Sciences 282 (2014) 111-135. doi: 10.1016/j.ins.2014.05.042
  
• [1782] A. Fernandez, D. Peralta, J.M. Benítez, F. Herrera. E-learning and educational data mining in cloud computing: an overview. International Journal of Learning Technology, 9:1 (2014) 25-52.
  
• [1803] V. López, S. Río, J.M. Benítez, F. Herrera. On the use of MapReduce to build Linguistic Fuzzy Rule Based Classification Systems for Big Data. 2014 IEEE World Congress on Computational Intelligence (WCCI 2014). IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2014), Beijing (China), pp. 1905-1912, 6-11 July, 2014.
  
• [1810] A. Fernandez, S. Río, V. López, A. Bawakid, M.J. del Jesus, J.M. Benítez, F. Herrera. Big Data with Cloud Computing: An Insight on the Computing Environment, MapReduce and Programming Frameworks. WIREs Data Mining and Knowledge Discovery 4:5 (2014) 380-409. doi: 10.1002/widm.1134
  
• [1817] L. S. Riza, Andrzej Janusz, C. Bergmeir, C. Cornelis, F. Herrera, Dominik Slezak, J.M. Benítez. Implementing algorithms of rough set theory and fuzzy rough set theory in the R package "RoughSets". Information Sciences 287 (2014) 68-89.
  

2013 (6)

• [1561] R. García, J.M. Benítez, G.I. Sainz-Palmero. FRASel: A Consensus of feature ranking methods for time series modelling. Soft Computing, Volume 17, Issue 8, pp 1489-1510.
  
• [1628] C. Bergmeir, Gregorio Sáinz, Carlos Martínez Bertrand, J.M. Benítez. A Study on the Use of Machine Learning Methods for Incidence Prediction in High-Speed Train Tracks. In: Proceedings IEA/AIE 2013, June 17-21, Amsterdam (The Netherlands).
  
• [1661] L.S. Riza, C. Bergmeir, F. Herrera, and J.M. Benítez. Constructing fuzzy rule-based systems with the R package frbs. Proceedings useR! 2013, July 10-12, Albacete (Spain).
  
• [1662] C. Bergmeir, J.M. Benítez, J. Bermúdez, J.V. Segura, and E. Vercher. Rsiopred: An R package for forecasting by exponential smoothing with model selection by a fuzzy multicriteria approach. Proceedings useR! 2013, July 10-12, Albacete (Spain).
  
• [1705] S. Río, V. López, J.M. Benítez, F. Herrera. Aplicando Métodos de Aprendizaje Sensible al Coste para Mejorar Problemas de Big Data Extremadamente Desbalanceados Usando Random Forest. IV Congreso Español de Informática (CEDI 2013). XV Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2013), Madrid (Spain), pp. 109-118, 17-20 September, 2013.
  
• [1707] C. Bergmeir, M. Costantini, J.M. Benítez. On the usefulness of the cross-validation for directional forecast evaluation. 7th International Conference on Computational and Financial Econometrics 2013, December 2013, London.
  

2012 (9)

• [1438] C. Bergmeir, J.M. Benítez. Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS. Journal of Statistical Software 46(7) (2012) 1-26.
  
• [1464] C. Bergmeir, M. García-Silvente, J.M. Benítez. Segmentation of Cervical Cell Nuclei in High-resolution Microscopic Images: A new Algorithm and a Web-based Software Framework. Computer Methods and Programs in Biomedicine 107(3) (2012) 497-512.
  
• [1472] C. Bergmeir, J.M. Benítez. On the Use of Cross-validation for Time Series Predictor Evaluation. Information Sciences 191 (2012) 192-213.
  
• [1506] J.L. Aznarte M., J. Alcalá-Fdez, A. Arauzo, J.M. Benítez. Financial Time Series Forecasting with a Bio-inspired Fuzzy Model. Expert Systems with Applications 39:16 (2012) 12302–12309. doi: 10.1016/j.eswa.2012.02.135
  
• [1545] A. Fernandez, S. Río, F. Herrera, J.M. Benítez. An Overview on the Structure and Applications for Business Intelligence and Data Mining in Cloud Computing. 7th International Conference on Knowledge Management in Organizations: Service and Cloud Computing (KMO'12), Salamanca (Spain), pp. 559-570, 11-13 July 2012.
  
• [1546] A. Fernandez, D. Peralta, F. Herrera, J.M. Benítez. An Overview of E-Learning in Cloud Computing. Workshop on Learning Technology for Education in Cloud (LTEC'12), Salamanca (Spain), pp. 35-46, 11-13 Julio 2012.
  
• [1549] C. Bergmeir, I. Triguero, F. Velasco, J.M. Benítez. Optimization of neuro-coefficient smooth transition autoregressive models using differential evolution. In Proceedings of the Seventh International Conference on Hybrid Artificial Intelligence Systems (HAIS 2012), March 28-30, Salamanca (Spain), Lecture Notes in Computer Science 7208, 464-473.
  
• [1550] C. Bergmeir, I. Triguero, D. Molina, J.L. Aznarte M., J.M. Benítez. Time Series Modeling and Forecasting Using Memetic Algorithms for Regime-switching Models. IEEE Transactions on Neural Networks and Learning Systems (2012), volume 23, issue 11, pages 1841-1847. doi: 10.1109/TNNLS.2012.2216898
  
• [1551] C. Bergmeir, I. Triguero, F. Velasco, J.M. Benítez. Optimización de Modelos Estadísticos y Difusos para el Análisis de Series Temporales Mediante Evolución Diferencial. Actas del XVI Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF2012), Valladolid (Spain), February 2012.
  

2011 (5)

• [1279] J.L. Aznarte M., J. Alcalá-Fdez, A. Arauzo, J.M. Benítez. Fuzzy autoregressive rules: Towards linguistic time series modeling. Econometric Reviews 30:6 (2011) 609–631. doi: 10.1080/07474938.2011.553569
  
• [1436] J.L. Aznarte M., D. Molina, A.M. Sánchez, J.M. Benítez. A test for the homoscedasticity of the residuals in fuzzy rule-based models. Applied Intelligence, 34:3 (2011), 386--393. doi: 10.1016/j.ejor.2011.04.018
  
• [1437] A. Araúzo-Azofra, J.L. Aznarte M., J.M. Benítez. Empirical study of feature selection methods based on individual feature evaluation for classification problems. Expert Systems with Applications, 38:7 (2011), 8170--8177.
  
• [1465] J.M. Benítez, J.L. Aznarte M., C. Bergmeir. Research on Time Series at the DiCITS Lab. Actas de la XIV Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA11), Tenerife (Spain), November 2011 .
  
• [1471] C. Bergmeir, J.M. Benítez. Forecaster Performance Evaluation with Cross-validation and Variants. 11th International Conference on Intelligent Systems Design and Applications (ISDA 2011), Córdoba (Spain), pp. 849-854, 22–24 November 2011.
  

2010 (6)

• [1193] J.L. Aznarte M., M.C. Medeiros, J.M. Benítez. Linearity testing against a fuzzy rule-based model. Fuzzy Sets and Systems Volume 161, Issue 13, 1 July 2010, Pages 1836-1851. doi: 10.1016/j.fss.2010.01.00
  
• [1221] I. Robles, R. Alcalá, J.M. Benítez, F. Herrera. On the Use of Distributed Genetic Algorithms for the Tuning of Fuzzy Rule Based-Systems. F. Fernández de Vega, E.Cantú-Paz (Eds.): Parallel and Distributed Computational Intelligence, Studies in Computational Intelligence 269, pp. 235-261, Springer-Verlag Berlin Heidelberg.
  
• [1288] J.L. Aznarte M., Marcelo C. Medeiros, J.M. Benítez. Testing for Remaining Autocorrelation of the Residuals in the Framework of Fuzzy Rule-Based Time Series Modelling. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 18(4), pp. 371-387. doi: 10.1142/S021848851000660X
  
• [1289] J.L. Aznarte M., J.M. Benítez. Equivalences between neural-autoregressive time series models and fuzzy systems. IEEE Transactions on Neural Networks, Vol. 21, No. 9 (2010), 1434-1445. doi: 10.1109/TNN.2010.2060209
  
• [1351] C. Bergmeir, M. García Silvente, J. Esquivias López-Cuervo, and J.M. Benítez. Segmentation of cervical cell images using mean-shift filtering and morphological operators. Proc. SPIE, Vol. 7623, 76234C (2010);. doi: 10.1117/12.845587
  
• [1356] J.L. Aznarte M., J.M. Benítez. Testing for heteroskedasticity of the residuals in fuzzy rule-based models.. XXIII International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems, Córdoba, Spain, 2010..
  

2009 (6)

• [0900] I. Robles, J.M. Benítez, M. Lozano, F. Herrera. Implementación de un algoritmo genético distribuido para optimización de problemas reales. VI Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB'09), páginas 419-424.
  
• [1041] I. Robles, R. Alcalá, J.M. Benítez, F. Herrera. Distributed Genetic Tuning of Fuzzy Rule-Based Systems. In Proceedings of the Joint International Fuzzy Systems Association World Congress and the European Society for Fuzzy Logic and Technology Conference (IFSA/EUSFLAT 2009), 1740-1744, Lisbon (Portugal), 20-24 July 2009.
  
• [1079] I. Robles, R. Alcalá, J.M. Benítez, F. Herrera. Evolutionary Parallel and Gradually Distributed Lateral Tuning of Fuzzy Rule-Based Systems. Evolutionary Intelligence 2 (2009) 5-19. doi: 10.1007/s12065-009-0025-0
  
• [1191] J.L. Aznarte M., J.M. Benítez. Testing of serial independence of the residuals in the framework of fuzzy rule based time series modelling. ISDA 2009, Pisa, Italia.
  
• [1192] A. Arauzo-Azofra, J.L. Aznarte M., J.M. Benítez. Empirical study of individual feature evaluators and cutting criteria for feature selection in classification. ISDA 2009, Pisa.
  
• [1357] J.L. Aznarte M., J.M. Benítez. Testing for linear independence of the residuals in the framework of fuzzy rule-based models. . 2009 Ninth International Conference on Intelligent Systems Design and Applications, Pisa, Italy..
  

2008 (2)

• [0824] A. Araúzo, J.M. Benítez, J.L. Castro. Consistency measures for feature selection. Journal of Intelligent Information Systems 30:3 (2008) 273-292. doi: 10.1007/s10844-007-0037-0
  
• [0971] J.R. Ruiz, J. Ramirez-Lechuga, F.B. Ortega, J.Castro-Piñero, J.M. Benítez, A. Arauzo-Azofra, C. Sanchez, M. Sjöström, M.J. Castillo, A. Gutierrez, M. Zabala. Artificial neural network-based equation for estimating VO2max from the 20 m shuttle run test in adolescents. Artificial Intelligence in Medicine 44:3 (2008) 233-245. doi: 10.1016/j.artmed.2008.06.004
  

2007 (2)

• [0823] J.L. Aznarte, D. Nieto Lugilde, J.M. Benítez, F. Alba Sánchez, C. de Linares Fernández. Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models. Expert Systems with Applications 32 (2007) 1218-1225. doi: 10.1016/j.eswa.2006.02.011
  
• [0822] J.L. Aznarte, J.M. Benítez, J.L. Castro. Smooth Transition Autoregressive Models and Fuzzy Rule-based Systems: Functional Equivalence and Consequences. Fuzzy Sets and Systems 158 (2007) 2734-2745. doi: 10.1016/j.fss.2007.03.021
  

2006 (4)

• [0785] P.L. Benitez, A.M. Guadix, E.M. Guadix, J.M. Benítez, J.M. Puche, J.M. Garcia. Detection of olive oil adulteration with hazelnut oil with computation intelligence tools. 4TH EuroFed Lipid Congress: Oils, Fats and Lipids for a Healthier Future 39-39.
  
• [0784] J.L. Aznarte, J.M. Benítez. On the Identifiability of TSK Additive Fuzzy Rule-Based Models. Advances in Soft Computing 6, 79–86, 2006.
  
• [0783] J.M. Puche, J.M. Benítez, J. L. Castro, C. J. Mantas. Un nuevo modelo formal de agregación difusa para máquinas vectores soporte multicategoría uno frente a uno. Proceedings of the XIII Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2006), Ciudad Real (Spain),, 100 - 105, September 2006.
  
• [0781] J.M. Puche, J.M. Benítez, J.L. Castro, C.J. Mantas. Fuzzy Pairwise Multiclass Support Vector Machines. MICAI 2006 Lecture Notes on Artificial Intelligence 4293, 562–571.
  

2005 (4)

• [0789] J.L. Aznarte, D. Nieto, J.M. Benítez, C. de Linares. Neuro-Fuzzy prediction of airborne pollen concentrations. 4th Conference of the European Society for Fuzzy Logic and Technology EUSFLAT'05 1325 - 1330, Barcelona September 2005.
  
• [0788] A.B. Bailon, J.M. Benítez, J.M. Fernandez, M. Garcia, M. Gomez, J.F. Huete, J.M. Garcia. SAETA: Una herramienta informática para el aprendizaje y la enseñanza de Teoría de Algoritmos. Actas del VI Congreso Nacional de Informática Educativa. Simposio Nacional de Tecnologías de la Información y las Comunicaciones en la Educación, SINTICE2005(ADIE), 251 - 258.
  
• [0787] J.M. Benítez, J.L. Castro, J.M. Puche. Building Fuzzy Classifiers with Pairwise Multiclass Support Vector Machines. International Joint EUSFLAT-LFH, Barcelona (Spain) September 2005, 1331-1336.
  
• [0786] J.L. Aznarte, J.M. Benítez, J.L. Castro,. Equivalence Relationships between Fuzzy Additive Systems for Time Series Analysis and Smooth Transition Models. Fuzzy Logic, Soft Computing and Computational Intelligence, 359-364.
  

2004 (8)

• [0816] J.L. Aznarte, J.M. Benítez, J.L. Castro. Equivalencia funcional entre General Regression Neural Networks y sistemas de inferencia difusa. XII Congreso Español sobre Tecnologías y Lógica Fuzzy, 599-604.
  
• [0794] J.L. Castro, J.M. Benítez, R. Valenzuela. Computación flexible aplicada al Web Mining. XII Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2004), 495 - 502, Jaen (Spain) 2004.
  
• [0793] J.L. Aznarte, J.M. Benítez, J.L. Castro,. Eguivalencia entre GRNN y Sistemas de Inferencia Difusos. XII Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2004), 599 - 604, Jaen (Spain) 2004.
  
• [0792] J.L. Aznarte, J.M. Benítez, J.L. Castro,. Functional equivalence between general regression Neural Networks and Fuzzy Inference Systems. Proceedings of the 10th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2004), 2205 - 2212.
  
• [0791] D. Nieto, J.L. Aznarte, F. Alba, J.M. Benítez, C. Díaz , C. De Linares. Modelling and Forecasting Olea europaea L. airborne pollen concentration in Granada (Southern Spain) using Soft Computing. XI International Palynologycal Congress. 372-373, Granada (Spain) July 2004..
  
• [0790] H. Latorre, J. L. Castro, J.M. Benítez, M. García. Un Modelo Difuso Para Focalización En Sistema De Visión Activa. XII Congreso Español Sobre Tecnologías Y Lógica Fuzzy, 189-194, Jaén 2004. .
  
• [0782] A. Arauzo, J.M. Benítez, J.L. Castro,. A Feature Set Measure Based on Relief. Proceedings of the Fifth International Conference on Recent Advances in Soft Computing, 104-109 .
  
• [0959] J.F. Fernández-Sánchez, A. Segura, J.M. Benitez, C. Cruces-Blanco, A. Fdez-Gutiérrez. Fluorescence optosensor using an artificial neural network for screening of polycyclic aromatic hydrocarbons. Analytica Chimica Acta 510 (2004) 183–187. doi: 10.1016/j.aca.2004.01.012
  

2003 (5)

• [0001] R. Alcalá, J.M. Benítez, J. Casillas, O. Cordón, R. Pérez. Fuzzy Control of HVAC Systems Optimized by Genetic Algorithms. Applied Intelligence 18:2 (2003) 155-177. doi: 10.1023/A:1021986309149
  
• [0127] R. Alcalá, J.M. Benítez, J. Casillas, J.L. Castro, O. Cordón, A. González, F. Herrera, R. Pérez. Multicriteria genetic tuning for the optimization and control of HVAC systems. In X. Yu, J. Kacprzyk (Eds.): Applied decision support with soft computing, Studies in Fuzziness and Soft Computing Vol. 124, Springer, Heidelberg, Germany, 2003, 308-345. ISBN 978-3-540-02491-0.
  
• [0798] A. Arauzo, J.M. Benítez, J.L. Castro. A feature selection algorithm with Fuzzy information. PROCEEDINGS OF THE 10-TH IFSA WORLD CONGRESS, 220 - 223, 2003.
  
• [0797] F. Pardo, A.M. Guadix, P.L. Benitez, J.M. Benítez, E.M. Guadix,. Artificial Neural Networks applied to the kinetics of enzymatic protein hydrolysis. PROCEEDINGS OF THE 4TH EUROPEAN CONGRESS ON CHEMICAL ENGINEERING, 2003.
  
• [0795] J.F. Fernandez-Sanchez, A. Segura, M.C. Cruces, A. Fdez-Gutierrez, J.M. Benítez. Fluorescence optosensor using an artificial neural network for screening of polycyclic aromatic hydrocarbons. COLLOQUIUM SPECTROSCOPICUM INTERNATIONALE XXXIII, 248 - 249, 2003.
  

2002 (5)

• [0821] J.L. Castro, C.J. Mantas, J.M. Benítez. Interpretation of Artificial Neural Networks by means of Fuzzy Rules. IEEE Transactions on Neural Networks 13:1 (2002) 101-116. doi: 10.1109/72.977279
  
• [0801] A. Salinas, A. Segura, J.M. Benítez, M.C. Cruces, A. Fdez-Gutierrez. Application of Artificial Neural Networks for the simultaneous determination of polycyclicaromatic hidrocarbons by heavy-atom induced room-temperature phosphorescence. X INTERNATIONAL SYMPOSIUM ON LUMINESCENCE SPECTROMETRY-DETECTION TECHNIQUES IN FLOWING STREAMS-QUALITY ASSURANCE AND APPLIED ANALYSIS, 254 - 254, 2002.
  
• [0800] P.L. Benitez, L. Cabrera, J. Jimenez, E.M. Guadix, J.M. Benítez. Artificial Neural Networks applied to the detection of olive oil adulteration with hazelnut oil. 15TH INTERNATIONAL CONGRESS OF CHEMICAL AND PROCESS ENGINEERING (CHISA 2002), 225 - 225, 2002.
  
• [0799] M. Melgosa, R. Huertas, E. Hita, J.M. Benítez. Diferencias de Color CIE94 con/sin un Estímulo de Referencia. VI Congreso Nacional de Color. Libro de Actas, 117-118. Sevilla September 2002..
  
• [0796] A. Arauzo, J.M. Benítez, J.L. Castro,. C-FOCUS: A continuous extension of FOCUS. PROCEEDINGS OF THE 7TH ONLINE WORLD CONFERENCE ON SOFT COMPUTING IN INDUSTRIAL APPLICATIONS, 225 - 232, 2003.
  

2001 (2)

• [0803] J.M. Benítez, J.L. Castro, C.J. Mantas. A Neuro-Fuzzy approach for feature selection. JOINT 9TH IFSA WORLD CONGRESS AND 20TH NAFIPS INTERNATIONAL CONFERENCE, 1121 - 1126, 2001.
  
• [0802] P.L. Benitez, E.M. Guadix, F. Rojas, J.M. Benítez. Evaluación y clasificación de aceites de oliva vírgenes extra utilizando Redes Neuronales Artifciales. I CONGRESO NACIONAL DE CIENCIA Y TECNOLOGIA DE LOS ALIMENTOS, 228 - 228, 2001.
  

2000 (4)

• [0820] J.L. Castro, C.J. Mantas, J.M. Benítez. Neural Networks with a Continuous Increasing Squashing function in the output are Universal Approximators. Neural Networks 13:6 (2000) 561-563. doi: 10.1016/S0893-6080(00)00031-9
  
• [0806] C.J. Mantas, J.L. Castro, J.M. Benítez. Multilayer Neural Networks as Fuzzy Rule Systems. 8th Information Processing and Management of Uncertainty in Knowledge-Based Systems Conference (IPMU 2000), 2205 - 2212, Madrid SPAIN, July 3-7, 2000.
  
• [0805] J.M. Benítez, J.L. Castro, C.J. Mantas. Un algoritmo de poda para perceptrones multicapa. X Congreso Español sobre Técnologías y Lógica Fuzzy (ESTYLF’2000), 91-96, Sevilla, 20-22 Sept. 2000..
  
• [0804] C.J. Mantas, J.L. Castro, J.M. Benítez, J.M. Mantas. Las Redes Neuronales Artificiales Multicapa son Sistemas Basados en Reglas Difusas encadenados. X Congreso Español sobre Técnologías y Lógica Fuzzy (ESTYLF 2000), 97 - 102 Sevilla, 20-22 Sept. 2000.
  

1999 (4)

• [0810] J.M. Benítez, J.L. Castro, C.J. Mantas. A link prunning algorithm for Neural Networks. PROCEEDINGS DEL EUSFLAT-ESTYLF JOINT CONFERENCE 99, 473 - 476, 1999.
  
• [0809] A. González, R. Pérez, J.M. Zurita, J.M. Benítez, C.J. Mantas, J.L. Castro, L. Castillo, F. Rojas. Contradiction sensitive Fuzzy Model-Based adaptative control. INFONAV: UNPROTOTIPO DE SISTEMA DE INFORMACIÓN PARA LA NAVEGACIÓN 0 - 0 1999.
  
• [0808] J.M. Fernández, J.M. Benítez, I. Requena. Una metodología de construcción de sistemas de clasificación basados en Reglas Difusas. PROCEEDINGS DEL EUSFLAT-ESTYLF JOINT CONFERENCE 99, 183 - 186, 1999.
  
• [0807] A. González, R. Pérez, J.M. Zurita, J.M. Benítez, C.J. Mantas, J.L. Castro, L. Castillo, F. Rojas. A Fuzzy control based algorithm to train perceptrons. INFONAV: UNPROTOTIPO DE SISTEMA DE INFORMACIÓN PARA LA NAVEGACIÓN 0 - 0 1999.
  

1998 (3)

• [0819] J.M. Benítez, A. Blanco, M. Delgado, I. Requena. New Aspects on Extraction of Fuzzy Rules using Neural Networks. Mathware & Soft Computing 5 (1998) 333-343.
  
• [0812] I. Requena, J.M. Benítez, E. López,. Asignación de semánticas en procesos de clasificación sin claves previas. VIII Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF’98), 183-188 Pamplona Septiembre 1998.
  
• [0811] J.M. Benítez, J.L. Castro, I. Requena. ARDIES: Ajuste de Reglas Difusas basado en Enfriamiento Simulado. VIII Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF’98). 205-210, Pamplona Septiembre, 1998.
  

1997 (4)

• [0818] J.M. Benítez, J.L. Castro, I. Requena. Are Artificial Neural Networks Black Boxes?. IEEE Transactions on Neural Networks 8:5 (1997) 1156-1164. doi: 10.1109/72.623216
  
• [0815] J.M. Benítez, J.L. Castro, I. Requena. A distributed Fuzzy Rule extraction method. Proceedings IFSA'97, 389 - 393, Praga 1997.
  
• [0814] J.M. Benítez, A. Blanco, M. Delgado, I. Requena,. Nuevos aspectos de extracción de Reglas Difusas usando Redes Neuronales. VII CONGRESO ESPAÑOL SOBRE TECNOLOGÍAS Y LÓGICA FUZZY (ESTYLF'97), 183 - 187, Sept. 1997.
  
• [0813] J.M. Benítez, A. Blanco, M. Delgado, I. Requena,. Extracting and refining Fuzzy Rules by means of Artificial Neural Networks. PROCEEDINGS OF IMACS'97, 0 - 0, 1997.
  

1996 (1)

• [0817] J.M. Benítez, A. Blanco, M. Delgado, I. Requena. Neural Methods for Obtaining Fuzzy Rules. Mathware & Soft Computing 3 (1996) 371-382.