Papers published in Journals (J. Luengo)

Number of Results: 55

Jump to Year: 2023, 2022, 2021, 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009

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2023 (4)

• [3068] I.Sevillano-García, J. Luengo, F. Herrera. REVEL Framework to Measure Local Linear Explanations for Black-Box Models: Deep Learning Image Classification Case Study. International Journal of Intelligent Systems 2023, 1-34. doi: 10.1155/2023/8068569
  
• [3069] Ignacio Aguilera-Martos, A.M. García-Vico, J. Luengo, S. Damas, F.J. Melero, J.J. Valle-Alonso, F. Herrera. TSFEDL: A python library for time series spatio-temporal feature extraction and prediction using deep learning. Neurocomputing 517, 223-228. doi: 10.1016/j.neucom.2022.10.062
  
• [3070] I. Aguilera-Martos, M. García-Bárzana, D. García-Gil, J. Carrasco, D. López, J. Luengo, F. Herrera. Multi-step histogram based outlier scores for unsupervised anomaly detection: ArcelorMittal engineering dataset case of study. Neurocomputing 544, 126228. doi: 10.1016/j.neucom.2023.126228
  
• [3071] D. López, I. Aguilera-Martos, M. García-Bárzana, F. Herrera, D. García-Gil, J. Luengo. Fusing anomaly detection with false positive mitigation methodology for predictive maintenance under multivariate time series. Information Fusion 100, 101957. doi: 10.1016/j.inffus.2023.101957
  

2022 (3)

• [2943] M.S. Santos, P.H. Abreu, A. Fernández, J. Luengo, J. Santos. The impact of heterogeneous distance functions on missing data imputation and classification performance. Engineering Applications of Artificial Intelligence, 111 (2022) 104791. doi: 10.1016/j.engappai.2022.104791
  
• [3075] G. González-Almagro, J.L. Suárez, J. Luengo, J.R. Cano, S. García. 3SHACC: Three stages hybrid agglomerative constrained clustering. Neurocomputing 490: 441-461 (2022). doi: 10.1016/j.neucom.2021.12.018
  
• [3076] J. Luengo, R. Moreno, I. Sevillano-García, D. Charte, A. Peláez-Vegas, M. Fernández-Moreno, P. Mesejo, F. Herrera. A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges. Information Fusion 78, 232-253. doi: 10.1016/j.inffus.2021.09.018
  

2021 (5)

• [3077] G. González-Almagro, J. Luengo, J.R. Cano, S. García. Enhancing instance-level constrained clustering through differential evolution. Applied Soft Computing 108, 107435. doi: 10.1016/j.asoc.2021.107435
  
• [3078] J. Carrasco, D. López, I. Aguilera-Martos, D. García-Gil, I. Markova, M. García-Bárzana, M. Arias-Rodil, J. Luengo, F. Herrera. Anomaly detection in predictive maintenance: A new evaluation framework for temporal unsupervised anomaly detection algorithms. Neurocomputing 462: 440-452 (2021). doi: 10.1016/j.neucom.2021.07.095
  
• [3079] M. González, J. Luengo, J. R. Cano, S. García. Synthetic Sample Generation for Label Distribution Learnin. Information Sciences 544: 197-213 (2021). doi: 10.1016/j.ins.2020.07.071
  
• [3080] J. Luengo, D. Sánchez Tarragó, R.C. Prati, F. Herrera. Multiple instance classification: Bag noise filtering for negative instance noise cleaning. Information Scences. 579: 388-400 (2021). doi: 10.1016/j.ins.2021.07.076
  
• [3081] G. González-Almagro, A. Rosales-Pérez, J. Luengo, J.R. Cano, S. García. ME-MEOA/DCC: Multiobjective constrained clustering through decomposition-based memetic elitism. Swarm Evolutionary Compututation 66: 100939 (2021).
  

2020 (4)

• [2790] J. Maillo, S. García, J. Luengo, F. Herrera, I. Triguero. Fast and Scalable Approaches to Accelerate the Fuzzy k Nearest Neighbors Classifier for Big Data. IEEE Transactions on Fuzzy Systems 28(5): 874-886 (2020). doi: 10.1109/TFUZZ.2019.2936356
  
• [3082] G. González-Almagro, J. Luengo, J. R. Cano, S. García. DILS: Constrained clustering through dual iterative local search. Computers & Operations Research 121: 104979 (2020). doi: 10.1016/j.cor.2020.104979
  
• [3083] J. A. Cortés-Ibáñez, S. González, J. J. Valle-Alonso, J. Luengo, S. García, F. Herrera. Preprocessing methodology for time series: An industrial world application case study. Inf. Sci. 514: 385-401 (2020). doi: j.ins.2019.11.027
  
• [3084] S. Tabik, A. Gómez-Ríos, J. L. Martín-Rodríguez, I. Sevillano-García, M. Rey-Area, D. Charte, E. Guirado, J.-L. Suárez, J. Luengo, M. A. Valero-González, P. García-Villanova, E. Olmedo-Sánchez, F. Herrera. COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images. IEEE Journal of Biomedical and Health Informatics 24(12): 3595-3605 (2020). doi: 10.1109/JBHI.2020.3037127
  

2019 (8)

• [2506] RC. Prati, J. Luengo, F. Herrera. Emerging topics and challenges of learning from noisy data in nonstandard classification: a survey beyond binary class noise. Knowledge and Information Systems 60(1): 63-97 (2019). doi: 10.1007/s10115-018-1244-4
  
• [2523] A. Gómez-Ríos, S. Tabik, J. Luengo, ASM. Shihavuddin, B. Krawczyk, F. Herrera. Towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation. Expert Systems with Applications 118 (2019) 315-328. doi: 10.1016/j.eswa.2018.10.010
  
• [2543] I. Triguero, D. García-Gil, J. Maillo, J. Luengo, S. García, F. Herrera. Transforming big data into smart data: An insight on the use of the k nearest neighbors algorithm to obtain quality data. Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery. e1289. doi: 10.1002/widm.1289
  
• [2557] D. García-Gil, J. Luengo, S. García, F. Herrera. Enabling smart data: noise filtering in big data classification. Information Sciences 479, 135-152. doi: 10.1016/j.ins.2018.12.002
  
• [2667] JR. Cano, J. Luengo, S. García. Label Noise Filtering Techniques to Improve Monotonic Classification. Neurocomputing 353: 83-95 (2019). doi: 10.1016/j.neucom.2018.05.131
  
• [3088] D. García-Gil, F. Luque Sánchez, J. Luengo, S. García, F. Herrera. From Big to Smart Data: Iterative ensemble filter for noise filtering in Big Data classification. International Journal of Intelligent Systems 34(12): 3260-3274 (2019). doi: 10.1002/int.22193
  
• [3089] I. Cordón, J. Luengo, S. García, F. Herrera, F. Charte. Smartdata: Data preprocessing to achieve smart data in R. Neurocomputing 360: 1-13 (2019). doi: 10.1016/j.neucom.2019.06.006
  
• [3090] A. Gómez-Ríos, S. Tabik, J. Luengo, A.S.M. Shihavuddin, F. Herrera. Coral species identification with texture or structure images using a two-level classifier based on Convolutional Neural Networks.. Knowledge-Based Systems 184 (2019). doi: j.knosys.2019.104891
  

2018 (1)

• [2383] J. Luengo, S.O. Shim, S. Alshomrani, A. Altalhi, F. Herrera. CNC-NOS: Class Noise Cleaning by Ensemble Filtering and Noise Scoring. Knowledge-Based Systems 140 (2018) 27-49. doi: 10.1016/j.knosys.2017.10.026
  OMPLEMENTARY MATERIAL to the paper: datasets, experimental results
  
  

2017 (3)

• [2133] S. García, S. Ramírez-Gallego, J. Luengo, F. Herrera. Big Data: Preprocesamiento y calidad de datos. Novática (Revista de la Asociación de Técnicos de Informática), Monografía Big Data, 237 (2017) 17-23..
  Enlace a la revista completa
  
  
• [2322] I. Triguero, S. González, J.M. Moyano, S. García, J. Alcalá-Fdez, J. Luengo, A. Fernández, M.J. del Jesús, L. Sánchez and F. Herrera. KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining. International Journal of Computational Intelligence Systems 10 (2017) 1238-1249.
  
• [2400] P. Morales, J. Luengo, L.P.F. Garcia, A.C. Lorena, A.C.P.L.F. de Carvalho and F. Herrera. The NoiseFiltersR Package: Label Noise Preprocessing in R. The R Journal 9:1 (2017) 219-228.
  

2016 (5)

• [1925] José A. Sáez, J. Luengo, F. Herrera. Evaluating the classifier behavior with noisy data considering performance and robustness: The Equalized Loss of Accuracy measure. Neurocomputing 176 (2016) 26-35. doi: 10.1016/j.neucom.2014.11.086
  
• [1924] José A. Sáez, M. Galar, J. Luengo, F. Herrera. INFFC: An iterative class noise filter based on the fusion of classifiers with noise sensitivity control. Information Fusion 27 (2016) 505-636. doi: 10.1016/j.inffus.2015.04.002
  
• [2014] S. García, J. Luengo, F. Herrera. Tutorial on practical tips of the most influential data preprocessing algorithms in data mining. Knowledge-Based Systems 98 (2016) 1–29. doi: 10.1016/j.knosys.2015.12.006
  COMPLEMENTARY MATERIAL to the paper: datasets, experimental results and source codes
  
  
• [2103] J. Luengo, A. M. García-Vico, M. D. Pérez-Godoy, C. J. Carmona. The influence of noise on the evolutionary fuzzy systems for subgroup discovery. Soft Computing 20:11 (2016) 4313-4330. doi: 10.1007/s00500-016-2300-1
  
• [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
  

2015 (3)

• [1699] J. Luengo, F. Herrera. An automatic extraction method of the domains of competence for learning classifiers using data complexity measures. Knowledge and Information Systems 42:1 (2015) 147-180. doi: 10.1007/s10115-013-0700-4
  
• [1824] José A. Sáez, J. Luengo, Jerzy Stefanowski, F. Herrera. SMOTE–IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering. Information Sciences 291 (2015) 184-203. doi: 10.1016/j.ins.2014.08.051
  COMPLEMENTARY MATERIAL to the paper
  
  
• [1963] L.P.F. Garcia, José A. Sáez, J. Luengo, A.C. Lorena, A.C. de Carvalho, F. Herrera. Using the One-vs-One decomposition to improve the performance of class noise filters via an aggregation strategy in multi-class classification problems. Knowledge-Based Systems 90 (2015) 153-164. doi: 10.1016/j.knosys.2015.09.023
  

2014 (3)

• [1557] José A. Sáez, M. Galar, J. Luengo, F. Herrera. Analyzing the Presence of Noise in Multi-class Problems: Alleviating its Influence with the One-vs-One Decomposition. Knowledge and Information Systems 38:1 (2014) 179-206. doi: 10.1007/s10115-012-0570-1
  COMPLEMENTARY MATERIAL to the paper
  
  
• [1646] I. Triguero, José A. Sáez, J. Luengo, S. García, F. Herrera. On the Characterization of Noise Filters for Self-Training Semi-Supervised in Nearest Neighbor Classification. Neurocomputing 132 (2014) 30-41. doi: 10.1016/j.neucom.2013.05.055
  
• [1791] José A. Sáez, J. Derrac, J. Luengo, F. Herrera. Statistical computation of feature weighting schemes through data estimation for nearest neighbor classifiers. Pattern Recognition 47:12 (2014) 3941–3948. doi: 10.1016/j.patcog.2014.06.012
  COMPLEMENTARY MATERIAL to the paper
  
  

2013 (3)

• [1469] S. García, J. Luengo, José A. Sáez, V. López, F. Herrera. A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning. IEEE Transactions on Knowledge and Data Engineering 25:4 (2013) 734-750. doi: 10.1109/TKDE.2012.35
  COMPLEMENTARY MATERIAL to the paper
  
  
• [1539] José A. Sáez, J. Luengo, F. Herrera. Predicting Noise Filtering Efficacy with Data Complexity Measures for Nearest Neighbor Classification. Pattern Recognition 46:1 (2013) 355-364. doi: 10.1016/j.patcog.2012.07.009
  COMPLEMENTARY MATERIAL to the paper
  
  
• [1655] José A. Sáez, M. Galar, J. Luengo, F. Herrera. Tackling the Problem of Classification with Noisy Data using Multiple Classifier Systems:Analysis of the Performance and Robustness. Information Sciences 247 (2013) 1-20. doi: 10.1016/j.ins.2013.06.002
  COMPLEMENTARY MATERIAL to the paper
  
  

2012 (4)

• [1408] J. Luengo, S. García, F. Herrera. On the choice of the best imputation methods for missing values considering three groups of classification methods. Knowledge and Information Systems 32:1 (2012) 77-108. doi: 10.1007/s10115-011-0424-2
  COMPLEMENTARY MATERIAL to the paper: Software, data sets, results and methods description
  
  
• [1429] J. Luengo, F. Herrera. Shared Domains of Competence of Approximative Models using Measures of Separability of Classes. Information Sciences 185:1 (2012) 43-65. doi: 10.1016/j.ins.2011.09.022
  
• [1430] J. Luengo, José A. Sáez, F. Herrera. Missing data imputation for Fuzzy Rule Based Classification Systems. Soft Computing 16 (2012) 863–881. doi: 10.1007/s00500-011-0774-4
  
• [1525] C. Carmona, J. Luengo, P. González, M.J. Del Jesus. An analysis on the use of pre-processing methods in evolutionary fuzzy systems for subgroup discovery. Expert Systems wit Applications 39 (2012) 11404–11412. doi: 10.1016/j.eswa.2012.04.029
  

2011 (3)

• [1276] J. Luengo, A. Fernandez, S. García, F. Herrera. Addressing Data Complexity for Imbalanced Data Sets: Analysis of SMOTE-based Oversampling and Evolutionary Undersampling. Soft Computing, 15 (10) (2011) 1909-1936. doi: 10.1007/s00500-010-0625-8
  
• [1277] J. Alcalá-Fdez, A. Fernandez, J. Luengo, J. Derrac, S. García, L. Sánchez, F. Herrera. KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework. Journal of Multiple-Valued Logic and Soft Computing 17:2-3 (2011) 255-287.
  SOFTWARE associated to the paper here
  
  
• [1342] S. García, J. Derrac, J. Luengo, C.J. Carmona, F. Herrera. Evolutionary Selection of Hyperrectangles in Nested Generalized Exemplar Learning. Applied Soft Computing 11:3 (2011) 3032-3045. doi: 10.1016/j.asoc.2010.11.030
  

2010 (4)

• [1043] J. Luengo, F. Herrera. Domains of Competence of Fuzzy Rule Based Classification Systems with Data Complexity measures: A case of study using a Fuzzy Hybrid Genetic Based Machine Learning Method. Fuzzy Sets and Systems, 161 (1) (2010) 3-19. doi: 10.1016/j.fss.2009.04.001
  
• [1104] A. Fernandez, S. García, J. Luengo, E. Bernadó-Mansilla, F. Herrera. Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy and Comparative Study. IEEE Transactions on Evolutionary Computation 14:6 (2010) 913-941. doi: 10.1109/TEVC.2009.2039140
  COMPLEMENTARY MATERIAL to the paper: dataset partitions, results, figures, etc
  
  
• [1112] J. Luengo, S. García, F. Herrera. A Study on the Use of Imputation Methods for Experimentation with Radial Basis Function Network Classifiers Handling Missing Attribute Values: The good synergy between RBFs and EventCovering method. Neural Networks 23 406-418. doi: 10.1016/j.neunet.2009.11.014
  COMPLEMENTARY MATERIAL to the paper: dataset partitions, results, figures, etc
  
  
• [1206] S. García, A. Fernandez, J. Luengo, F. Herrera. Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental Analysis of Power. Information Sciences 180 (2010) 2044–2064. doi: 10.1016/j.ins.2009.12.010
  COMPLEMENTARY MATERIAL to the paper: Software and tests description
  
  

2009 (2)

• [0893] J. Luengo, S. García, F. Herrera. A Study on the Use of Statistical Tests for Experimentation with Neural Networks: Analysis of Parametric Test Conditions and Non-Parametric Tests. Expert Systems with Applications 36 (2009) 7798-7808. doi: 10.1016/j.eswa.2008.11.041
  COMPLEMENTARY MATERIAL to the paper: Software and tests description
  
  
• [0898] S. García, A. Fernandez, J. Luengo, F. Herrera. A Study of Statistical Techniques and Performance Measures for Genetics-Based Machine Learning: Accuracy and Interpretability. Soft Computing 13:10 (2009) 959-977. doi: 10.1007/s00500-008-0392-y
  COMPLEMENTARY MATERIAL to the paper: Software and tests description