SCI2S Publications (J. Luengo)

Number of Results: 101

Jump to Year: 2023, 2022, 2021, 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007

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2023 (7)

• [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
  
• [3072] I. Aguilera-Martos, J. Luengo, F. Herrera. Revisiting Histogram Based Outlier Scores: Strengths and Weaknesses. HAIS 2023: 39-48. doi: 10.1007/978-3-031-40725-3_4
  
• [3073] I. Sevillano-García, J. Luengo, F. Herrera. Optimizing LIME Explanations Using REVEL Metrics. HAIS 2023: 304-313. doi: https://doi.org/10.1007/978-3-031-40725-3_26
  
• [3074] I. Sevillano-García, J. Luengo, F. Herrera. Low-Impact Feature Reduction Regularization Term: How to Improve Artificial Intelligence with Explainability.. xAI (Late-breaking Work, Demos, Doctoral Consortium) 2023: 135-139.
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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 (7)

• [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
  
• [3085] G. González-Almagro, A. Rosales-Pérez, J. Luengo, J.R. Cano, S. García. Improving constrained clustering via decomposition-based multiobjective optimization with memetic elitism. GECCO 2020: 333-341. doi: 10.1145/3377930.3390187
  
• [3086] G. González-Almagro, J.-L. Suárez, J. Luengo, J.R. Cano, S. García. Agglomerative Constrained Clustering Through Similarity and Distance Recalculation. HAIS 2020: 424-436. doi: 10.1007/978-3-030-61705-9_35
  
• [3087] J.R. Cano, J. Luengo, S. García. Similarity-based and Iterative Label Noise Filters for Monotonic Classification. HICSS 2020: 1-9.
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2019 (10)

• [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
  
• [3091] B. Montesdeoca, J. Luengo, J. Maillo, D. García-Gil, S. García, F. Herrera. A First Approach on Big Data Missing Values Imputation. IoTBDS 2019: 315-323. doi: 10.5220/0007738403150323
  
• [3092] D. García-Gil, A. Alcalde-Barros, J. Luengo, S. García, F. Herrera. Big Data Preprocessing as the Bridge between Big Data and Smart Data: BigDaPSpark and BigDaPFlink Libraries. IoTBDS 2019: 324-331. doi: 10.5220/0007738503240331
  

2018 (7)

• [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
  
  
• [2511] J. Maillo, J. Luengo, S. Garcia, F. Herrera, I. Triguero. A preliminary study on Hybrid Spill-Tree Fuzzy k-Nearest Neighbors for big data classification. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2018), Rio de Janeiro (Brazil), July 8-13. doi: 10.1109/FUZZ-IEEE.2018.8491595
  
• [2514] J. Maillo, J. Luengo, S. Garcia, F. Herrera, I. Triguero.. Un enfoque aproximado para acelerar el algoritmo de clasificacion Fuzzy kNN para Big Data. II Workshop en Big Data y Análisis de Datos Escalable (BigDADE 2018), Granada (España), 23-26 octubre 2018.
  
• [2558] J. Luengo, D. Sanchez-Tarrago, RC. Prati, F. Herrera. A First Study on the Use of Noise Filtering to Clean the Bags in Multi-Instance Classification. LOPAL '18 Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications. doi: 10.1145/3230905.3230911
  
• [2560] RC. Prati, J. Luengo, F. Herrera. Emerging topics and challenges of learning fromnoisy data in non-standard classification: A surveybeyond binary class noise. IX Simposio de Teoría y Aplicaciones de la Minería de Datos (TAMIDA 2018) pp. 889-890.
  
• [2583] D. García-Gil, J. Luengo, S. García, F. Herrera. Smart Data: Filtrado de Ruido para Big Data. XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA2018), II Workshop en Big Data y Análisis de Datos Escalable, Octubre 23-26, 2018.
  
• [2602] A. Gómez-Ríos, S. Tabik, J. Luengo, ASM Shihavuddin, B. Krawczyk, F. Herrera. Redes Neuronales Convolucionales para una Clasificación Precisa de Imágenes de Corales. XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2018), I Workshop en Deep Learning (DEEPL 2018), Octubre 23-26, 2018.
  

2017 (5)

• [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
  
  
• [2304] A. Gómez-Ríos, J. Luengo, F. Herrera. A Study on the Noise Label Influence in Boosting Algorithms: AdaBoost, GBM and XGBoost. 12th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2017), Lecture Notes in Computer Science (LNCS 10334), Logroño, Spain, June 21-23 June, 2017.. doi: 10.1007/978-3-319-59650-1_23
  
• [2310] J. Maillo, J. Luengo, S. Garcia, F. Herrera, I. Triguero. Exact Fuzzy k-Nearest Neighbor Classification for Big Datasets. IEEE Conference on Fuzzy Systems (FUZZ-IEEE 2017), Naples (Italy), July 9-12.
  
• [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 (7)

• [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
  
• [2098] P. Morales, J. Luengo, F. Herrera. A First Study on the Use of Boosting for Class Noise Reparation. 11th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2016), Lecture Notes in Computer Science (LNCS) 9648, Seville (Spain), 549-559, April 18-20, 2016. doi: 10.1007/978-3-319-32034-2_46
  
• [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
  
• [2138] I. Triguero, J. Maillo, J. Luengo, S. García, F. Herrera. From Big data to Smart Data with the K-Nearest Neighbours algorithm. The 2016 IEEE International Conference on Smart Data (SmartData 2016), Chengdu (China), Dec 16-19, 2016. doi: 10.1109/iThings-GreenCom-CPSCom-SmartData.2016.177
  
• [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 (5)

• [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
  
  
• [1940] C. Carmona, J. Luengo. A First Approach in the Class Noise Filtering Approaches for Fuzzy Subgroup Discovery. 10th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2015), Advances in Intelligent Systems and Computing 368, Burgos (Spain), 387-399, June 15-17, 2015. doi: 10.1007/978-3-319-19719-7_34
  
• [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
  
• [2060] S. García, J. Luengo, F. Herrera. Data Preprocessing in Data Mining. Intelligent Systems Reference Library. Series Editors: Kacprzyk, Janusz, Jain, Lakhmi C. ISSN: 1868-4394.
  

2014 (5)

• [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
  
  
• [1792] José A. Sáez, J. Derrac, J. Luengo, F. Herrera. Improving the behavior of the nearest neighbor classifier against noisy data with feature weighting schemes. Hybrid Artificial Intelligent Systems 2014 (HAIS 2014), Lecture Notes in Computer Science Volume 8480, 2014, pp 597–606.
  
• [1828] José A. Sáez, J. Luengo, Jerzy Stefanowski, F. Herrera. Managing Borderline and Noisy Examples in Imbalanced Classification by Combining SMOTE with Ensemble Filtering. Intelligent Data Engineering and Automated Learning 2014 (IDEAL 2014), Lecture Notes in Computer Science Volume 8669, 2014, pp 61-68.
  

2013 (4)

• [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
  
  
• [1743] José A. Sáez, Mikel Galar, J. Luengo, F. Herrera. An Experimental Case of Study on the Behavior of Multiple Classifier Systems with Class Noise Datasets. Hybrid Artificial Intelligent Systems 2013 (HAIS 2013), Lecture Notes in Computer Science Volume 8073, 2013, pp 568-577 .
  

2012 (9)

• [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
  
• [1451] J. Derrac, J. Luengo, A. Fernandez, S. García, J. Alcalá-Fdez. KEEL: Una herramienta docente para sistemas difusos. XVI Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2012), Valladolid (España), pp. 534-539, 1-3 Febrero.
  
• [1498] José A. Sáez, J. Luengo, F. Herrera. Sistemas de clasificación basados en reglas difusas y sistemas nítidos robustos entrenados en presencia de ruido de clase: un caso de estudio. XVI Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2012), Valladolid (España), pp. 602-607, 1-3 Febrero 2012.
  
• [1505] S. García, V. López, J. Luengo, C.J. Carmona, F. Herrera. A Preliminary Study on Selecting the Optimal Cut Points in Discretization by Evolutionary Algorithms. 1st International Conference on Pattern Recognition Applications and Methods (ICPRAM2012), Vilamoura (Portugal), pp. 211-216, 6-8 February 2012.
  
• [1517] José A. Sáez, M. Galar, J. Luengo, F. Herrera. A First Study on Decomposition Strategies with Data with Class Noise Using Decision Trees. In Proceedings of the Seventh International Conference on Hybrid Artificial Intelligence Systems (HAIS 2012), March 28-30, Salamanca (Spain), Lecture Notes in Computer Science 7209, 25-35.
  
• [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
  
• [1744] C.J. Carmona, J. Luengo, P. Gonzalez, M.J. del Jesus. A preliminary study on missing data imputation in evolutionary fuzzy systems of subgroup discovery. In Proceedings of 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'12), Brisbane (Australia), 10-15 June, pp 1--7.
  

2011 (6)

• [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
  
• [1397] J. Luengo. Soft Computing based learning and Data Analysis: Missing Values and Data Complexity. Department of Computer Science and Artificial Intelligence, University of Granada.
  Advisor: F. Herrera
  
• [1440] J. Derrac, J. Luengo, J. Alcalá-Fdez, A. Fernandez, S. García. Using KEEL Software as a Educational Tool: A Case of Study Teaching Data Mining. Second International Conference on EUropean Transnational Education (ICEUTE 2011), Salamanca (Spain), pp. 55-60, October 20-21, 2011.
  
• [1453] José A. Sáez, J. Luengo, F. Herrera. Fuzzy Rule Based Classification Systems versus Crisp Robust Learners Trained in Presence of Class Noise's Effects: a Case of Study. 11th International Conference on Intelligent Systems Design and Applications (ISDA 2011), Córdoba (Spain), pp. 1229-1234, 22–24 November 2011..
  

2010 (11)

• [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
  
  
• [1250] J. Derrac, A. Fernandez, J. Luengo, S. García, L. Sánchez, J. Alcalá-Fdez, F. Herrera. KEEL: Una herramienta software para el análisis de sistemas difusos evolutivos. XV Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2010), Huelva (Spain), 417-422, 3-5 February 2010..
  
• [1251] J. Luengo, F. Herrera. Determinando Automáticamente los Dominios de Competencia de un Sistema de Clasificación Basado en Reglas Difusas: Un Caso de Estudio con FH-GBML. XV Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2010), Huelva (Spain), 235-240, 3-5 February 2010.
  
• [1309] J. Luengo, F. Herrera. Obtención de los dominios de competencia de C4.5 por medio de medidas de separabilidad de clases. In Proceedings of the III Congreso Español de Informática (CEDI 2010). VIII Jornadas de Aplicaciones y Transferencia Tecnológica de la Inteligencia Artificia (TTIA 2010) Valencia (Spain), 33-44, 7-10 September 2010. .
  
• [1310] J. Luengo, F. Herrera. An Extraction Method for the Characterization of the Fuzzy Rule Based Classification Systems’ Behavior using Data Complexity Measures: A case of study with FH-GBML. In Proceedings on the WCCI 2010 IEEE World Congress on Computational Intelligence, IEEE Congress on Fuzzy Logic FUZZ-IEEE'2010, Barcelona (Spain), 18-23 July, pp 702-709.
  
• [1311] M. Galar, José A. Sáez, J. Luengo, F. Herrera. Influencia del Ruido en Sistemas de Clasificación con Múltiples Clases: Análisis sobre la estrategia Uno-contra-Uno. In Proceedings of the III Congreso Español de Informática (CEDI 2010). V Simposio de Teoría y Aplicaciones de Minería de Datos (TAMIDA 2010) Valencia (Spain), 65-74, 7-10 September 2010.
  
• [1312] José A. Sáez, J. Luengo, F. Herrera. Análisis del impacto del ruido en clases y atributos para Sistemas de Clasificación Basados en Reglas Difusas. In Proceedings of the III Congreso Español de Informática (CEDI 2010).III Simposio sobre Lógica Fuzzy y Soft Computing, LFSC2010 (EUSFLAT), Valencia (Spain), 467-474, 7-10 September 2010.
  
• [1348] José A. Sáez, J. Luengo and F. Herrera. A First Study on the Noise Impact in Classes for Fuzzy Rule Based Classification Systems. In Proceedings of 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering (ISKE2010). IEEE Press. November 15-16, 2010, Hangzhou (China), pp. 153-158.
  

2009 (7)

• [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
  
  
• [1062] J. Luengo, F. Herrera. Domains of Competence of Artificial Neural Networks Using Measures of Separability of Classes. 10th International Work-Conference on Artificial Neural Networks (IWANN09) Lecture Notes on Computer Science 5517, Springer-Verlag, Salamanca (Spain) 81-88, June 2009.
  
• [1077] J. Luengo, F. Herrera. On the use of Measures of Separability of Classes to characterise the Domains of Competence of a Fuzzy Rule Based Classification System. Proceedings of the 2009 International Fuzzy Systems Association congress and 2009 European Society for Fuzzy Logic and Technology conference, Lisbon (Portugal) 1027-1032, July 2009.
  
• [1078] A. Fernandez, J. Luengo, J. Derrac, J. Alcalá-Fdez and F. Herrera. Implementation and Integration of Algorithms into the KEEL Data-Mining Software Tool. 10th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2009). Lecture Notes in Computer Science 5788, Springer 2009, Burgos (Spain, 2009) 562-569.
  
• [1176] J. Luengo, A. Fernandez, F. Herrera, S. García. Addressing Data-Complexity for Imbalanced Data-sets: A Preliminary Study on the Use of Preprocessing for C4.5. 9th International Conference on Intelligent Systems Designs and Applications (ISDA'09), Pisa (Italy) November 2009, 523-528 .
  
• [1177] S. García, J. Derrac, J. Luengo, F. Herrera. A First Approach to Nearest Hyperrectangle Selection by Evolutionary Algorithms. 9th International Conference on Intelligent Systems Designs and Applications (ISDA'09), Pisa (Italy) November 2009, 517-522.
  

2008 (1)

• [0892] J. Luengo, S. García, J.R. Cano, F. Herrera. Estudio de la influencia de las medidas de complejidad de los datos en los Sistemas de Clasifcación Basados en Reglas Difusas: Análisis de la Razón Discriminante de Fisher. XIV Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF08) Mieres (Spain), 257-263, 17-19 September 2008.
  

2007 (2)

• [0722] J. Luengo, S. García, F. Herrera. Estudio de la influencia de los métodos de imputación en Redes Neuronales de Base Radial para clasificación. Proceedings of the II Congreso Español de Informática (CEDI 2007). Simposio de Inteligencia Computacional (SICO2007), Zaragoza (Spain), 81-88, 11-14 September 2007.
  
• [0725] J. Luengo, S. García, F. Herrera. A Study on the Use of Statistical Tests for Experimentation with Neural Networks. Proceedings of the 9th International Work-Conference on Artificial Neural Networks (IWANN07). Lecture Notes on Computer Science 4507, Springer-Verlag, San Sebastián (Spain), 72-79, June 2007.