Papers published in Journals (J.M. Benítez)

Number of Results: 58

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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 (4)

• [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
  

2016 (6)

• [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
  
• [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 (9)

• [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
  
• [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
  

2014 (9)

• [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.
  
• [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 (1)

• [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.
  

2012 (5)

• [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
  
• [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
  

2011 (3)

• [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.
  

2010 (3)

• [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
  
• [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
  

2009 (1)

• [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
  

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
  

2004 (1)

• [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 (1)

• [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
  

2002 (1)

• [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
  

2000 (1)

• [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
  

1998 (1)

• [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.
  

1997 (1)

• [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
  

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.