Papers published in Journals (F. Herrera)

Number of Results: 471

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In Press (6)

• [2573] A. Valdivia, E. Martínez-Cámara, I. Chaturvedi, M.V. Luzón, E. Cambria, Y. Ong, F. Herrera. What do people think about this monument? Understanding negative reviews via deep learning, clustering and descriptive rules. Journal of Ambient Intelligence and Humanized Computing (2018), in press. doi: 10.1007/s12652-018-1150-3
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• [2578] A. Valdivia, E. Hrabova, I. Chaturvedi, M.V. Luzón, L. Troiano, E. Cambria, F. Herrera. Inconsistencies on TripAdvisor Reviews: a Unified Index between Users and Sentiment Analysis Methods. Neurocomputing (2019), in press.
  
• [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
  
• [2361] F. Charte, A.J. Rivera, M.J. del Jesus, F. Herrera. Dealing with Difficult Minority Labels in Imbalanced Mutilabel Data Sets. Neurocomputing, 2017. Available online 14 September 2017, in press. doi: 10.1016/j.neucom.2016.08.158
  
• [2362] F. Charte, A.J. Rivera, M.J. del Jesus, F. Herrera. REMEDIAL-HwR: Tackling Multilabel Imbalance through Label Decoupling and Data Resampling Hybridization. Neurocomputing, 2017. Available online 12 September 2017, in press. doi: 10.1016/j.neucom.2017.01.118
  

2023 (8)

• [2975] C. Zuheros, E. Martinez-Camara, E. Herrera-Viedma, F. Herrera. Crowd Decision Making: Sparse Representation guided by Sentiment Analysis for leveraging the Wisdom of the Crowd. IEEE Transactions on Systems, Man and Cybernetics:Systems, Volume 53, Issue: 1, Pages 369-379, 2022. Accepted.. doi: 10.1109/TSMC.2022.3180938
  
• [2989] J. Deng, J. Zhan, E. Herrera-Viedma, F. Herrera. Regret theory-based three-way decision method on incomplete multi-scale decision information systems with interval fuzzy numbers. IEEE Transactions on Fuzzy Systems, vol. 31, no. 3, pp. 982-996, March 2023. doi: 10.1109/TFUZZ.2022.3193453
  
• [3014] J. Poyatos, D. Molina, A. D. Martínez, J. Del Ser, F. Herrera. EvoPruneDeepTL: An evolutionary pruning model for transfer learning based deep neural networks. Neural Networks, 158 (2023) 59-82. doi: 10.1016/j.neunet.2022.10.011
  
• [3056] J. Poyatos, D. Molina, Aitor Martínez-Seras, J. Del Ser, F. Herrera. Multiobjective evolutionary pruning of Deep Neural Networks with Transfer Learning for improving their performance and robustness. Applied Soft Computing 147 (2023) 110757 1-12. doi: 10.1016/j.asoc.2023.110757
  
• [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 (4)

• [2939] S. Maldonado, C. Vairetti, A. Fernandez, F. Herrera. FW-SMOTE: a feature-weighted oversampling approach for imbalanced classification. Pattern Recognition, 124, 108511:1-16 (2022). doi: 10.1016/j.patcog.2021.108511
  
• [2957] D. Molina, J. Poyatos, E. Osaba, J. Del Ser, F. Herrera. Nature-And Bio-Inspired Optimization: The Good, The Bad, The Ugly And The Hopeful. Dyna, 97:2 (2022) 114-117. doi: 10.6036/10331
  
• [2958] X. Liu, N. Wang, D. Molina, F. Herrera. A least square support vector machine approach based on bvRNA-GA for modeling photovoltaic systems. Applied Soft Computing, 117 (2022) 108357. doi: 10.1016/j.asoc.2021.108357
  
• [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 (15)

• [2895] E. Osaba, E. Villar-Rodriguez, J. Del Ser, A. J. Nebro, D. Molina, A. La Torre, P.N. Suganthan, C.A. Coello, F. Herrera. A Tutorial on the Design, Experimentation and Application of Metaheuristic Algorithms to Real-World Optimization Problems. Swarm and Evolutionary Computation.64 (2021), 100888. doi: 10.1016/j.swevo.2021.100888
  
• [2836] E. León-Castro, L.F. Espinoza-Audelo, J.M. Merigó, E. Herrera-Viedma, F. Herrera. Measuring volatility based on ordered weighted average operators: Agricultural products prices case of use. Fuzzy Sets and Systems 422 (2021) 161-176. doi: 10.1016/j.fss.2020.08.006
  
• [2853] A. D. Martinez, J. del Ser, E. Villa-Rodriguez, E. Osaka, J. Poyatos, S. Tabik, D. Molina, F. Herrera. Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons,Recommendations and Challenges. Information Fusion 67 (2021) 161-194. doi: 10.1016/j.inffus.2020.10.014
  
• [2873] E. Herrera-Viedma, I. Palomares, C.-C. Li, F.J. Cabrerizo, Y.C. Dong, F. Chiclana, F. Herrera. Revisiting fuzzy and linguistic decision making: Scenarios and challenges for making wiser decisions in a better way. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51:1 (2021) 191-208. doi: 10.1109/TSMC.2020.3043016
  
• [2898] J.D. Pascual, D. Charte, M. Andrés, A. Fernández, F. Herrera. Revisiting data complexity metrics based onmorphology for overlap and imbalance: snapshot, new overlap number of balls metrics and singular problems prospect. Knowledge and Information Systems 63 (2021) 1961–1989. doi: 10.1007/s10115-021-01577-1
  
• [2899] S. Alonso, R. Montes, D. Molina, I. Palomares, E. Martínez-Cámara, M. Chiachio, J. Chiachio, F.J. Melero, P. García-Moral, B. Fernández, C. Moral, R. Marchena, J. Pérez de Vargas, F. Herrera. Article Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys. Sustainability 2021, 13(11), 6038. doi: 10.3390/su13116038
  
• [2903] A. Lamas, S. Tabik, P. Cruz, R. Montes, A. Martínez-Sevilla, T. Cruz, F. Herrera. MonuMAI: Dataset, deep learning pipeline and citizen science based app for monumental heritage taxonomy and classification. Neurocomputing 420 (2021)266-280. doi: j.neucom.2020.09.041
  
• [2906] I. Palomares, E. Martínez-Cámara, R. Montes, P. García-Moral, M. Chiachio, J. Chiachio, S. Alonso, F.J. Melero, D. Molina, B. Fernández, C. Moral, R. Marchena, J.P. de Vargas, F. Herrera. A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects. Applied Intelligence, (2021) 51, 6497-6527. doi: 10.1007/s10489-021-02264-y
  
• [2920] A. LaTorre, D. Molina, E. Osaba, J. Poyatos, J. Del Ser, F. Herrera. A prescription of methodological guidelines for comparing bio-inspired optimization algorithms. Swarm and Evolutionary Computation, 67 (2021) 100973. doi: 10.1016/j.swevo.2021.100973
  
• [2921] J.A. Fdez-Sánchez, J.D. Pascual-Triana, A. Fernandez, F. Herrera. Learning interpretable multi-class models by means of hierarchical decomposition: Threshold Control for Nested Dichotomies. Neurocomputing 463 (2021) 514-524. doi: 10.1016/j.neucom.2021.07.097
  
• [2947] C. Zuheros, E. Martínez-Cámara, E. Herrera-Viedma, F. Herrera. Sentiment Analysis based Multi-Person Multi-criteria Decision Making methodology using natural language processing and deep learning for smarter decision aid. Case study of restaurant choice using TripAdvisor reviews. Information Fusion 68 (2021) 22-36. doi: 10.1016/j.inffus.2020.10.019
  
• [2948] X. Chao, G. Kou, Y. Peng, E. Herrera-Viedma, F. Herrera. An efficient consensus reaching framework for large-scale social network group decision making and its application in urban resettlement. Information Fusion 575 (2021) 499-527. doi: 10.1016/j.ins.2021.06.047
  
• [3067] N. Rodríguez, D. López, A. Fernández, S. García, F. Herrera. SOUL: Scala Oversampling and Undersampling Library for imbalance classification. SoftwareX 15 (2021) 100767. doi: 10.1016/j.softx.2021.100767
  
• [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
  
• [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
  

2020 (6)

• [2736] A. B. Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-Robles, D. Molina, R. Benjamins, R. Chatila, F. Herrera. Explainable ArtificialIntelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58 (2020) 82-115. doi: 10.1016/j.inffus.2019.12.012
  
• [2745] R-X. Ding, I. Palomares, X. Wang, G-R. Yang, B. Liu, Y. Dong, E. Herrera-Viedma, F. Herrera. Large-Scale decision-making: Characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective. Information Fusion 59 (2020) 84-102. doi: 10.1016/j.inffus.2020.01.006
  
• [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
  
• [2847] D. Molina, J. Poyatos, J.D. Del Ser, S. García, A. Hussain, F. Herrera. Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations. Cognitive Computation 12:5 (2020) 897-939. doi: 10.1007/s12559-020-09730-8
  
• [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 (20)

• [2335] S. Elhag, A. Fernandez, A. Altalhi, S. Alshomrani, F. Herrera. A Multi-Objective Evolutionary Fuzzy System to Obtain a Broad and Accurate Set of Solutions in Intrusion Detection Systems. Soft Computing 23:4 (2019) 1321-1336. doi: 10.1007/s00500-017-2856-4
  
• [2485] J. Cózar, A. Fernandez, F. Herrera, J.A. Gámez. A Meta-Hierarchical Rule Decision System to Design Robust Fuzzy Classifiers Based on Data Complexity. IEEE Transactions on Fuzzy Systems 27:4 (2019) 701-715. doi: 10.1109/TFUZZ.2018.2866967
  
• [2490] A. Fernandez, M.J. de Jesus, O. Cordon, F. Marcelloni, F. Herrera. Evolutionary Fuzzy Systems for Explainable Artificial Intelligence: Why, When, What for, and Where to?. IEEE Computational Intelligence Magazine 14:1 (2019) 69-81. doi: 10.1109/MCI.2018.2881645
  
• [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
  
• [2544] C-C Li, Y. Dong, Y. Xu, F. Chiclana, E. Herrera-Viedma, F. Herrera. An overview on managing additive consistency of reciprocal preference relations for consistency-driven decision making and Fusion: Taxonomy and future directions. Information Fusion, Vol. 52, 2019, Pages 143-156..
  
• [2545] X. Liu, Y. Xu, R. Montes, R. Ding, F. Herrera. Alternative Ranking-Based Clustering and Reliability Index-Based Consensus Reaching Process for Hesitant Fuzzy Large Scale Group Decision Making. IEEE Transactions on Fuzzy Systems 27:1, 159-171, January 2019. doi: 10.1109/TFUZZ.2018.2876655
  
• [2554] X. Liu, Y. Xu, R. Montes, Y. Dong, F. Herrera. Analysis of self-confidence indices-based additive consistency for fuzzy preference relations with self-confidence and its application in group decision making. Int J Intell Syst. 34:5 (2019) 920-946. doi: 10.1002/int.22081
  
• [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
  
• [2568] A. Castillo, S. Tabik, F. Pérez, R. Olmos, F. Herrera. Brightness guided preprocessing for automatic cold steel weapon detection in surveillance videos with deep learning. Neurocomputing 330(2019) 151-161. doi: 10.1016/j.neucom.2018.10.076
  Complementary material
  
  
• [2571] S. González, S. García, S-T. Li, F. Herrera. Chain based sampling for monotonic imbalanced classification. Information Sciences 474 (2019) 187-204. doi: 10.1016/j.ins.2018.09.062
  
• [2575] M. López, A. Valdivia, E. Martínez-Cámara, M.V. Luzón, F. Herrera. E2SAM: Evolutionary Ensemble of Sentiment Analysis Methods for Domain Adaptation. Information Sciences, 480 (2019) 273-286. doi: 10.1016/j.ins.2018.12.038
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• [2688] X. Liu, Y. Xu, R. Montes, F. Herrera. Social network group decision making: Managing self-confidence based consensus model with dynamic importance degree of experts and trust-based feedback mechanism. Information Sciences 505 (2019) 215-232. doi: j.ins.2019.07.050
  
• [2690] J. Del Ser, E. Osaba, D. Molina, Xin-She Yang, S. Salcedo-Sanz, D. Camacho, S. Das, P. N.Suganthan, C. A.Coello, F. Herrera. Bio-inspired computation: Where we stand and what's next. Swarm and Evolutionary Computation 48 (2019) 220-250. doi: 10.1016/j.swevo.2019.04.008
  
• [2691] M. Leon, N. Xiong, D. Molina, F. Herrera. A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search. International Journal of Computational Intelligence Systems 12:2 (2019) 795-808. doi: 10.2991/ijcis.d.190711.001
  
• [2708] A. Fernandez, I. Triguero, M. Galar, F. Herrera. Guest Editorial: Computational Intelligence for Big Data Analytics. Cognitive Computation 11 (2019) 329–330. doi: 10.1007/s12559-019-09647-x
  
• [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 (41)

• [2299] R. Montes, A.M. Sánchez, P. Villar, F. Herrera. Teranga Go!: Carpooling Collaborative Consumption Community with multi-criteria hesitant fuzzy linguistic term set opinions to build confidence and trust. Applied Soft Computing. Volume 67, June 2018, Pages 941-952. doi: 10.1016/j.asoc.2017.05.039
  
• [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
  
• [2324] S. Vluymans, A. Fernandez, C. Cornelis, Y. Saeys, F. Herrera. Dynamic Affinity-based Classification of Multi-Class Imbalanced Data with One-vs-One Decomposition: a Fuzzy Rough Set Approach. Knowledge and Information Systems 56:1 (2018) 55–84. doi: 10.1007/s10115-017-1126-1
  
• [2338] S. Ramírez-Gallego, A. Fernández, S. García, M. Chen, F. Herrera. Big Data: Tutorial and Guidelines on Information and Process Fusion for Analytics Algorithms with MapReduce. Information Fusion 42 (2018) 51-61. doi: 10.1016/j.inffus.2017.10.001
  
• [2345] F. Padillo, J.M. Luna, F. Herrera, S. Ventura. Mining Association Rules on Big Data through MapReduce Genetic Programming. Integrated Computer-Aided Engineering.
  
• [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
  
• [2365] C.J. Carmona, M.J. del Jesus, F. Herrera. A Unifying Analysis for the Supervised Descriptive Rule Discovery via the Weighted Relative Accuracy. Knowledge-Based Systems (2017). doi: 10.1016/j.knosys.2017.10.015
  
• [2372] R. Olmos, S. Tabik, F. Herrera. Automatic Handgun Detection Alarm in Videos Using Deep Learning. Neurocomputing 275, 66-72 (2018).. doi: 10.1016/j.neucom.2017.05.012
  
• [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
  
  
• [2386] H. Liao, Z. Xu, E. Herrera-Viedma, F. Herrera. Hesitant fuzzy linguistic term set and its application in decision making: A state-of-the-art survey. Int. J. of Fuzzy Systems, 2018, Volume 20, Issue 7, pp 2084–2110.. doi: 10.1007/s40815-017-0432-9
  
• [2431] A. Fernandez, S. Garcia, N.V. Chawla, F. Herrera. SMOTE for Learning from Imbalanced Data: Progress and Challenges. Marking the 15-year Anniversary. Journal of Artificial Intelligence Research 61 (2018) 863-905. doi: 10.1613/jair.1.11192
  
• [2449] D.G. Cañizares, A. Fernandez, F. Herrera, A. Antunes, R. Molina-Ruiz, G. Agüero-Chapin. Surveying Alignment-free Features for Ortholog Detection in Related Yeast Proteomes by using Supervised Big Data Classifiers. BMC Bioinformatics 19:166 (2018) 1-17. doi: 10.1186/s12859-018-2148-8
  
• [2462] D. Martín, M. Martínez-Ballesteros, D. García-Gil, J. Alcalá-Fdez, J.C. Riquelme-Santos, F. Herrera. MRQAR: a generic MapReduce framework to discover Quantitative Association Rules in Big Data problems. Knowledge-Based Systems 153 (2018) 176-192. doi: 10.1016/j.knosys.2018.04.037
  
• [2463] D. Molina, A. LaTorre, F. Herrera. An Insight into Bio-inspired and Evolutionary Algorithms for Global Optimization: Review, Analysis, and Lessons Learnt over a Decade of Competitions. Cognitive Computation (2018) 10:4, 517-544. doi: 10.1007/s12559-018-9554-0
  
• [2486] I. Cordon, S. Garcia, A. Fernandez, F. Herrera. imbalance: Oversampling Algorithms for Imbalanced Classification in R. Knowledge-Based Systems 161 (2018) 329-341. doi: 10.1016/j.knosys.2018.07.035
  
• [2569] A. Valdivia, M.V. Luzón, E. Cambria, F. Herrera. Consensus vote models for detecting and filtering neutrality in sentiment analysis. Information Fusion 44 (2018) 126-135. doi: 10.1016/j.inffus.2018.03.007
  
• [2570] Z-L. Zhang, X-G. Luo, S. González, S. García, F. Herrera. DRCW-ASEG: One-versus-One distance-based relative competence weighting with adaptive synthetic example generation for multi-class imbalanced datasets. Neurocomputing 285 (2018) 176-187. doi: 10.1016/j.neucom.2018.01.039
  
• [2576] M.J. Basgall, W. Hasperué, M. Naiouf, A. Fernandez, F. Herrera. SMOTE-BD: An Exact and Scalable Oversampling Method for Imbalanced Classification in Big Data. Journal of Computer Science & Technology 18:3 (2018) 203-209.
  
• [2580] D. García-Gil, S. Ramírez-Gallego, S. García, F. Herrera. Principal Components Analysis Random Discretization Ensemble for Big Data. Knowledge-Based Systems, 150, 2018, 166-174. doi: 10.1016/j.knosys.2018.03.012
  
• [2584] D. Charte, F. Charte, S. García, F. Herrera. A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations. Progress in Artificial Intelligence, 2018, 1-14. doi: 10.1007/s13748-018-00167-7
  
• [2585] S. Ramírez-Gallego, S. García, F. Herrera. Online entropy-based discretization for data streaming classification. Future Generation Computer Systems 86, 2018, 59-70. doi: 10.1016/j.future.2018.03.008
  
• [2586] S. García, ZL. Zhang, A. Altalhi, S. Alshomrani, F. Herrera. Dynamic ensemble selection for multi-class imbalanced datasets. Information Sciences 445, 2018, 22-37. doi: 10.1016/j.ins.2018.03.002
  
• [2589] B. Krawczyk, I. Triguero, S. García, M. Wozniak, F. Herrera. Instance reduction for one-class classification. Knowledge and Information Systems, 2018, 1-28. doi: 10.1007/s10115-018-1220-z
  
• [2592] A. Rosales-Perez, S. García, H. Terashima-Marin, CAC. Coello, F. Herrera. MC2ESVM: Multiclass Classification Based on Cooperative Evolution of Support Vector Machines. IEEE Computational Intelligence Magazine 13 (2), 2018, 18-29. doi: 10.1109/MCI.2018.2806997
  
• [2593] D. Charte, F. Charte, S. García, M. J. del Jesus, F. Herrera. A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines. Information Fusion 44, 2019, 78–96. doi: 10.1016/j.inffus.2017.12.007
  
• [2621] C. Zuheros, C.-C. Li, F.J. Cabrerizo, Y. Dong, E. Herrera-Viedma, F. Herrera. Computing with words: Revisiting the qualitative scale. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26:Suppl. 2 (2018) 127-143. doi: 10.1142/S0218488518400147
  
• [2643] C. Li, R. M. Rodríguez, F. Herrera, L. Martínez, Y. Dong. Consistency of hesitant fuzzy linguistic preference relations: An interval consistency index. Information Sciences, 432 (2018) 347-361. doi: 10.1016/j.ins.2017.12.018
  
• [2644] S. Vluymans, C. Cornelis, F. Herrera, Y. Saeys. Multi-Label Classification using a Fuzzy Rough Neighborhood Consensus. Information Sciences, vol. 433-434 (2018), 96-114.. doi: 10.1016/j.ins.2017.12.034
  
• [2645] M. Chen, F. Herrera, K. Hwang. Cognitive Computing: Human-Centered Computing with Cognitive Intelligence on Clouds. IEEE Access, 6 (2018) 19774 – 19783. doi: 10.1109/ACCESS.2018.2791469
  
• [2646] R. M. Rodríguez, Y. Xu, L. Martínez, F. Herrera. Exploring Consistency for Hesitant Preference Relations in Decision Making: Discussing Concepts, Meaning and Taxonomy.. Journal of multiple-valued logic and soft computing, 30:2-3 (2018) 129-154.
  
• [2647] G. Acampora, F. Herrera, G. Tortora, A. Vitiello. A multi-objective evolutionary approach to training set selection for support vector machine. Knowledge-Based Systems, 147 (2018) 94-108.. doi: 10.1016/j.knosys.2018.02.022
  
• [2648] I. Chaturvedi, E. Cambria, R. E. Welsch, F. Herrera. Distinguishing Between Facts and Opinions for Sentiment Analysis: Survey and Challenges. Information Fusion, 44 (2018) 65-77.. doi: 10.1016/j.inffus.2017.12.006
  
• [2650] X. Gou, Z. Xu, F. Herrera. Consensus Reaching Process for Large-scale Group Decision Making with Double Hierarchy Hesitant Fuzzy Linguistic Preference Relations. Knowledge-Based Systems, 157 (2018) 20-33. doi: 10.1016/j.knosys.2018.05.008
  
• [2653] B. Krawczyk, M. Galar, M. Wozniak, H. Bustince, F. Herrera. Dynamic ensemble selection for multi-class classification with one class classifiers. Pattern Recognition, 83 (2018) 34-51.. doi: 10.1016/j.patcog.2018.05.015
  
• [2654] A. Roldan, C. Roldán, F. Herrera.. On a new methodology for ranking fuzzy numbers and its application to real economic data. Fuzzy Sets and Systems, 353 (2018) 86-110. doi: 10.1016/j.fss.2018.04.003
  
• [2655] H. Liao, X. Wu, X. Liang, J.-B. Yang, D.-L. Xu, F. Herrera. A continuous interval-valued linguistic ORESTE method for multi-criteria group decision making. Knowledge-Based Systems, 153 (2018) 65-77. doi: 10.1016/j.knosys.2018.04.022
  
• [2656] C. Li, R. M. Rodríguez, L. Martínez, Y. Dong, F. Herrera. Personalized individual semantics based on consistency in hesitant linguistic group decision making with comparative linguistic expressions. Knowledge-Based Systems, 145 (2018), 156-165.. doi: 10.1016/j.knosys.2018.01.011
  
• [2657] H. C. Liao, X. M. Mi, Z. S. Xu, J. P. Xu, F. Herrera. Intuitionistic fuzzy analytic network process. IEEE Transactions on Fuzzy Systems, 26:5 (2018), 2578-2590. doi: 10.1109/TFUZZ.2017.2788881
  
• [2658] X. Wu, H. Liao, Z. Xu, A. Hafezalkotob, F. Herrera. Probabilistic Linguistic MULTIMOORA: A Multi-Criteria Decision Making Method Based on the Probabilistic Linguistic Expectation Function and the Improved Borda Rule. IEEE Transactions on Fuzzy Systems, 26:6 (2018), 3688-3702. doi: 10.1109/TFUZZ.2018.2843330
  
• [2660] X. Gou, Z. Xu, H. Liao, F. Herrera. Multiple criteria decision making based on distance and similarity measures under double hierarchy hesitant fuzzy linguistic environment. Computers & Industrial Engineering, 126 (2018) 516-530. doi: 10.1016/j.cie.2018.10.020
  
• [2661] Z. Fu, X. Wu, H. Liao, F. Herrera. Underground mining method selection with the hesitant fuzzy linguistic gained and lost dominance score method. IEEE Access, 6 (2018), 66442-66458. doi: 10.1109/ACCESS.2018.2878784
  

2017 (28)

• [2086] A. Fernandez, S. Río, F. Herrera. Fuzzy Rule Based Classification Systems for Big Data with MapReduce: Granularity Analysis. Advances in Data Analysis and Classification 11 (2017) 711-730. doi: 10.1007/s11634-016-0260-z
  
• [2089] J. Maillo, S. Ramírez-Gallego, I. Triguero, F. Herrera. kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data. Knowledge-Based Systems 117 (2017) 3-15. doi: 10.1016/j.knosys.2016.06.012
  
• [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
  
• [2100] Li, C-C., Y. Dong, F. Herrera, E. Herrera-Viedma, and L. Martínez. Personalized individual semantics in Computing with Words for supporting linguistic Group Decision Making. An Application on Consensus reaching. Information Fusion 33:1 (2017) 29-40.
  
• [2113] M. Galar, A. Fernandez, E. Barrenechea, H. Bustince, F. Herrera. NMC: Nearest Matrix Classification – A new combination model for pruning One-vs-One ensembles by transforming the aggregation problem. Information Fusion 36 (2017) 26–51. doi: 10.1016/j.inffus.2016.11.004
  
• [2135] A. Fernandez, S. Río, N.V. Chawla, F. Herrera. An insight into imbalanced Big Data classification: outcomes and challenges. Complex & Intelligent Systems 3:2 (2017) 105–120. doi: 10.1007/s40747-017-0037-9
  
• [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
  
  
• [2143] A. Fernandez, C.J. Carmona, M.J. del Jesus, F. Herrera. A Pareto Based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets. International Journal of Neural Systems 27 (2017) 1750028-1:1750028-21. doi: 10.1142/S0129065717500289
  
• [2150] Z-L. Zhang, X-G. Luo, S. García, F. Herrera. Cost-Sensitive back-propagation neural networks with binarization techniques in addressing multi-class problems and non-competent classifiers. Applied Soft Computing 56 (2017) 357-367. doi: 10.1016/j.asoc.2017.03.016
  
• [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
  
• [2153] S. Ramírez-Gallego, B. Krawczyk, S. García, M. Wozniak, F. Herrera. A survey on data preprocessing for data stream mining: Current status and future directions. Neurocomputing 239 (2017) 39-57. doi: 10.1016/j.neucom.2017.01.078
  
• [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
  
• [2156] S. González, S. García, M. Lázaro, A.. Figueiras-Vidal, F. Herrera. Class Switching according to Nearest Enemy Distance for learning from highly imbalanced data-sets. Pattern Recognition 70 (2017) 12-24. doi: 10.1016/j.patcog.2017.04.028
  
• [2158] Z-L. Zhang, X-G. Luo, S. García, J-F. Tang, F. Herrera. Exploring the effectiveness of dynamic ensemble selection in the one-versus-one scheme. Knowledge-Based Systems 125 (2017) 53-63. doi: 10.1016/j.knosys.2017.03.026
  
• [2159] A. Rosales-Perez, S. García, J.A. Gonzalez, C.A.C. Coello, F. Herrera. An Evolutionary Multi-Objective Model and Instance Selection for Support Vector Machines with Pareto-based Ensembles. IEEE Transactions on Evolutionary Computation (Volume: 21, Issue: 6, Dec. 2017) 863-877. doi: 10.1109/TEVC.2017.2688863
  
• [2160] D. García-Gil, S. Ramírez-Gallego, S. García, F. Herrera. A comparison on scalability for batch big data processing on Apache Spark and Apache Flink. Big Data Analytics 2:1 (2017) 1. doi: 10.1186/s41044-016-0020-2
  
• [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
  
• [2190] S. Tabik, D. Peralta, A. Herrera-Poyatos, F. Herrera. A snapshot of image pre-processing for convolutional neural networks: case study of MNIST. International Journal of Computational Intelligence Systems 10 (2017) 555-568.
  
• [2323] A. Fernandez, A. Altalhi, S. Alshomrani, F. Herrera. Why Linguistic Fuzzy Rule Based Classification Systems perform well in Big Data Applications?. International Journal of Computational Intelligence Systems 10 (2017) 1211-1225. doi: 10.2991/ijcis.10.1.80
  
• [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.
  
• [2355] C. García-Martínez, P.D. Gutiérrez, D. Molina, M. Lozano, F. Herrera. Since CEC 2005 competition on realparameter optimisation: a decade of research, progress and comparative analysis's weakness. Soft Computing, 21:19 (2017) 5573-5583. doi: 10.1007/s00500-016-2471-9
  
• [2356] A. Alonso-Betanzos, J.A. Gámez, F. Herrera, J.M. Puerta, J.C. Riquelme. Volume, variety and velocity in Data Science. Knowledge-Based Systems 117 (2017) 1-2. doi: 10.1016/j.knosys.2016.11.005
  
• [2357] J.A. Gámez, F. Herrera, J.M. Puerta. Guest Editorial: Recent Trends in Intelligent Systems. International Journal of Intelligent Systems 32 (2) (2017) 107-108. doi: 10.1002/int.21831
  
• [2358] X. Gou, H. Liao, Z. Xu, F. Herrera. Double hierarchy hesitant fuzzy linguistic term set and MULTIMOORA method: A case of study to evaluate the implementation status of haze controlling measures. Information Fusion 38 (2017) 22-34. doi: 10.1016/j.inffus.2017.02.008
  
• [2359] H. Liao, L. Jiang, Z. Xu, J. Xu, F. Herrera. A linear programming method for multiple criteria decision making with probabilistic linguistic information. Information Sciences 415 (2017) 341-355. doi: 10.1016/j.ins.2017.06.035
  
• [2360] Y. Dong, Z. Ding, L. Martínez, F. Herrera. Managing consensus based on leadership in opinion dynamics. Information Sciences 398 (2017) 187–205. doi: 10.1016/j.ins.2017.02.052
  
• [2366] A. Valdivia, M.V. Luzón, F. Herrera. Sentiment Analysis in TripAdvisor. IEEE Intelligent Systems 32:4 (2017) 72-77. doi: 10.1109/MIS.2017.3121555
  
• [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 (25)

• [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
  
• [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
  
• [2079] D. Martin, J. Alcalá-Fdez, A. Rosete, F. Herrera. NICGAR: a Niching Genetic Algorithm to Mine a Diverse Set of Interesting Quantitative Association Rules. Information Sciences 355-356 (2016) 208-228. doi: 10.1016/j.ins.2016.03.039
  COMPLEMENTARY MATERIAL to the paper: datasets, experimental results, etc
  
  
• [1964] J. Derrac, F. Chiclana, S. García, F. Herrera. Evolutionary fuzzy k-nearest neighbors algorithm using interval-valued fuzzy sets. Information Sciences 329 (2016) 144-163. doi: 10.1016/j.ins.2015.09.007
  
• [1965] Y. Xu, F. Herrera, H. Wang. A distance-based framework to deal with ordinal and additive inconsistencies for fuzzy reciprocal preference relations. Information Sciences 380:20 (2016) 189-205. doi: 10.1016/j.ins.2015.08.034
  
• [1966] J. Mendel, H. Hagras, H. Bustince Sola, F. Herrera. Comments on “Interval type-2 fuzzy sets are generalization of interval-valued fuzzy sets: towards a wide view on their relationship”[2]. IEEE Transactions on Fuzzy Systems 24:1 (2016) 249-250. doi: 10.1109/TFUZZ.2015.2446508
  
• [1968] N. Verbiest, J. Derrac, C. Cornelis, S. García, F. Herrera. Evolutionary Wrapper Approaches for Training Set Selection as Preprocessing Mechanism for Support Vector Machines: Experimental Evaluation and Support Vector Analysis. Applied Soft Computing 38 (2016) 10-22. doi: 10.1016/j.asoc.2015.09.006
  
• [2070] A. Fernandez, M. Elkano, M. Galar, J.A. Sanz, S. Alshomrani, H. Bustince, F. Herrera. Enhancing Evolutionary Fuzzy Systems for Multi-Class Problems: Distance-based Relative Competence Weighting with Truncated Confidences (DRCW-TC). International Journal of Approximate Reasoning 73 (2016) 108-122. doi: 10.1016/j.ijar.2016.02.005
  
• [1996] S. Ramírez-Gallego, S. García, H. Mouriño Talín, D. Martínez-Rego, V. Bolón-Canedo, A. Alonso-Betanzos, J. M. Benítez, F. Herrera. Data discretization: taxonomy and big data challenge. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 6:1 (2016) 5-21. doi: 10.1002/widm.1173
  
• [2009] E. Ramentol, I. Gondres, S. Lajes, R. Bello, Y. Caballero, C. Cornelis, F. Herrera. Fuzzy-rough imbalanced learning for the diagnosis of High Voltage Circuit Breaker maintenance: The SMOTE-FRST-2T algorithm. Engineering Applications of Artificial Intelligence 48 (2016) 134-139. doi: 10.1016/j.engappai.2015.10.009
  
• [2011] B. Krawczyk, M. Galar, L. Jelén, F. Herrera. Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy. Applied Soft Computing 38 (2016) 714-726. doi: 10.1016/j.asoc.2015.08.060
  
• [2013] S. Vluymans, D. Sanchez-Tarrago, Y. Saeys, C. Cornelis, F. Herrera. Fuzzy Multi-Instance Classifiers. IEEE Transactions on Fuzzy Systems 24:6 (2016) 1395-1409. doi: 10.1109/TFUZZ.2016.2516582
  
• [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
  
  
• [2015] R.M. Rodríguez, B. Bedregal, H. Bustince, Y.C. Dong, B. Farhadinia, C. Kahraman, L. Martínez, V. Torra, Y.J. Xu, Z.S. Xu, F. Herrera. A position and perspective analysis of hesitant fuzzy sets on information fusion in decision making. Towards high quality progress. Information Fusion 29 (2016) 89-97. doi: 10.1016/j.inffus.2015.11.004
  
• [2016] S. Vluymans, D. Sánchez-Tarragó, Y. Saeys, C. Cornelis, F. Herrera. Fuzzy rough classifiers for class imbalanced multi-instance data. Pattern Recognition 53 (2016) 36-45. doi: 10.1016/j.patcog.2015.12.002
  
• [2017] H. Bustince, E. Barrenechea, M. Pagola, J. Fernandez, Z. Xu, B. Bedregal, J. Montero, H. Hagras, F. Herrera, B. De-Baets. A historical account of types of fuzzy sets and their relationships. IEEE Transactions on Fuzzy Systems 24:1 (2016) 179-194. doi: 10.1109/TFUZZ.2015.2451692
  
• [2073] M. Galar, A. Fernandez, E. Barrenechea, H. Bustince, F. Herrera. Ordering-Based Pruning for Improving the Performance of Ensembles of Classifiers in the Framework of Imbalanced Datasets. Information Sciences 354 (2016) 178-196. doi: 10.1016/j.ins.2016.02.056
  
• [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
  
• [2082] A. Fernandez, C.J. Carmona, M.J. del Jesus, F. Herrera. A View on Fuzzy Systems for Big Data: Progress and Opportunities. International Journal of Computational Intelligence Systems, 9:1 (2016), 69-80. doi: 10.1080/18756891.2016.1180820
  
• [2109] B. Lacroix, D. Molina, F. Herrera. Region-based memetic algorithm with archive for multimodal optimisation. Information Sciences 367-368 (2016) 719-746. doi: 10.1016/j.ins.2016.05.049
  
• [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
  
• [2164] Z-L. Zhang, B. Krawczyk, S. García, A. Rosales-Pérez, F. Herrera. Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data. Knowledge-Based Systems 106 (2016) 251-263. doi: 10.1016/j.knosys.2016.05.048
  
• [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
  
  
• [2261] X. Yejun, L. Chen, R.M. Rodríguez, F. Herrera, H. Wang. Deriving the priority weights from incomplete hesitant fuzzy preference relations in group decision making. Knowledge-Based Systems 99 (2016) 71-78. doi: 0950-7051
  

2015 (34)

• [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
  
• [1683] I. Triguero, S. García, F. Herrera. Self-Labeled Techniques for Semi-Supervised Learning: Taxonomy, Software and Empirical Study. Knowledge and Information Systems 42 (2015) 245-284. doi: 10.1007/s10115-013-0706-y
  COMPLEMENTARY MATERIAL to the paper: datasets, experimental results and source codes
  
  
• [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
  
• [1769] I. Triguero, D. Peralta, J. Bacardit, S. García, F. Herrera. MRPR: A MapReduce Solution for Prototype Reduction in Big Data Classification. Neurocomputing 150 (2015), 331-345. doi: 10.1016/j.neucom.2014.04.078
  
• [1788] I. Triguero, S. García, F. Herrera. SEG-SSC: A Framework based on Synthetic Examples Generation for Self-Labeled Semi-Supervised Classification. IEEE Transactions on Cybernetics 45:4 (2015) 622-634. doi: 10.1109/TCYB.2014.2332003
  
• [1790] F. Charte, A.J. Rivera, M.J. del Jesus, F. Herrera. Addressing imbalance in multilabel classification: Measures and random resampling algorithms. Neurocomputing 163 (2015) 3-16. doi: 10.1016/j.neucom.2014.08.091
  
• [1811] M. Galar, A. Fernandez, E. Barrenechea, F. Herrera. DRCW-OVO: Distance-based Relative Competence Weighting Combination for One-vs-One Strategy in Multi-class Problems. Pattern Recognition 48 (2015) 28-42. doi: 10.1016/j.patcog.2014.07.023
  
• [1813] S. Elhag, A. Fernandez, A. Bawakid, S. Alshomrani, F. Herrera. On the Combination of Genetic Fuzzy Systems and Pairwise Learning for Improving Detection Rates on Intrusion Detection Systems. Expert Systems with Applications 42:1 (2015) 193–202. doi: 10.1016/j.eswa.2014.08.002
  
• [1814] J.A. Sanz, D. Bernardo, F. Herrera, H. Bustince, H. Hagras. A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications with Imbalanced Data. IEEE Transactions on Fuzzy Systems 23:4 (2015) 973-990. doi: 10.1109/TFUZZ.2014.2336263
  
• [1815] Y. Dong, C.-C. Li, F. Herrera. An optimization-based approach to adjusting unbalanced linguistic preference relations to obtain a required consistency level. Information Sciences 292 (2015) 27-38. doi: 10.1016/j.ins.2014.08.059
  
• [1819] S. Alshomrani, A. Bawakid, S.-O. Shim, A. Fernandez, F. Herrera. A Proposal for Evolutionary Fuzzy Systems using Feature Weighting: Dealing with Overlapping in Imbalanced Datasets. Knowledge-Based Systems 73 (2015) 1-17. doi: 10.1016/j.knosys.2014.09.002
  
• [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
  
  
• [1834] H. Bustince, J. Fernandez, H. Hagras, F. Herrera. Interval Type-2 Fuzzy Sets are generalization of Interval-Valued Fuzzy Sets: Towards a Wider view on their relationship. IEEE Transactions on Fuzzy Systems 23:5 (2015) 1876-1882. doi: 10.1109/TFUZZ.2014.2362149
  
• [1843] M. Elkano, M. Galar, J.A. Sanz, A. Fernandez, E. Barrenechea, F. Herrera, H. Bustince. Enhancing multi-class classification in FARC-HD fuzzy classifier: On the synergy between n-dimensional overlap functions and decomposition strategies. IEEE Transactions on Fuzzy Systems 23:5 (2015) 1562-1580. doi: 10.1109/TFUZZ.2014.2370677
  
• [1845] Y. Dong, X. Chen, F. Herrera. Minimizing adjusted simple terms in the consensus reaching process with hesitant linguistic assessments in group decision making. Information Sciences 297 (2015) 95–117. doi: 10.1016/j.ins.2014.11.011
  
• [1846] E. Ramentol, S. Vluymans, N. Verbiest, Y. Caballero, R. Bello, C. Cornelis, F. Herrera. IFROWANN: Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor Classification. IEEE Transactions on Fuzzy Systems 23:5 (2015) 1622-1637. doi: 10.1109/TFUZZ.2014.2371472
  
• [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
  
• [1863] A. Fernandez, V. López, M.J. del Jesus, F. Herrera. Revisiting Evolutionary Fuzzy Systems: Taxonomy, Applications, New Trends and Challenges. Knowlegde Based Systems 80 (2015) 109–121. doi: 10.1016/j.knosys.2015.01.013
  
• [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
  
  
• [1897] Ning Xiong, D. Molina, Miguel Leon Ortiz, F. Herrera. A Walk into Metaheuristic for Engineering Optimization: Principles, Methods and Recent Trends. International Journal of Computational Intelligence Systems 8:4 (2015) 606-636. doi: 10.1080/18756891.2015.1046324
  
• [1921] D. Peralta, M. Galar, I. Triguero, D. Paternain, S. García, E. Barrenechea, J. M. Benítez, H. Bustince, F. Herrera. A Survey on Fingerprint Minutiae-based Local Matching for Verification and Identification: Taxonomy and Experimental Evaluation. Information Sciences 315 (2015) 67-87. doi: 10.1016/j.ins.2015.04.013
  COMPLEMENTARY MATERIAL to the paper: experimental results and statistical tests
  
  
• [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
  
• [1937] B. Krawczyk, M. Wozniak, F. Herrera. On the Usefulness of One-Class Classifier Ensembles for Decomposition of Multi-Class Problems. Pattern Recognition 48:12 (2015) 3969-3982. doi: 10.1016/j.patcog.2015.06.001
  
• [1938] R. Montes, A.M. Sánchez, P. Villar, F. Herrera. A web tool to support decision making in the housing market using hesitant fuzzy linguistic term sets. Applied Soft Computing 35 (2015) 949-957. doi: 10.1016/j.asoc.2015.01.030
  
• [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
  
• [1951] D. Galpert, S. Río, F. Herrera, E. Ancede, A. Antunes, G. Agüero-Chapin. An Effective Big Data Supervised Imbalanced Classification Approach for Ortholog Detection in Related Yeast Species. BioMed Research International (2015) 12 pages, Article Id 748681. doi: 10.1155/2015/748681
  
• [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
  
• [1967] F. Charte, A.J. Rivera, M.J. del Jesus, F. Herrera. MLSMOTE: Approaching Imbalanced Multilabel Learning Through Synthetic Instance Generation. Knowledge-Based Systems 89 (2015) 385-397. doi: 10.1016/j.knosys.2015.07.019
  
• [1980] S. González, F. Herrera, S. García. Monotonic Random Forest with an Ensemble Pruning Mechanism based on the Degree of Monotonicity. New Generation Computing 33:4 (2015) 367-388. doi: 10.1007/s00354-015-0402-4
  
• [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
  
• [2061] F. Charte, A.J. Rivera, M.J. del Jesus, F. Herrera. Addressing imbalance in multilabel classification: Measures and random resampling algorithms. Neurocomputing 163 (2015) 3-16. doi: doi:10.1016/j.neucom.2014.08.091
  
• [2185] F. Rojas Ruiz, A. Cano, M. Gomez Olmedo, J. Ortega Lopera, F. Herrera, R. Romero Zaliz, J. Gonzalez Peñalver. Máster en Ciencia de Datos e Ingeniería de Computadores: una apuesta por la formación especializada en el sector de las TIC. Journal of Educational Experiences on Computer Engineering (JEECE), Volumen 5, Numero 1, pp. 5-16, 2015, ISSN 2173-8688, Universidad de Granada, Departamento de Arquitectura y Tecnologia de Computadoras.
  

2014 (34)

• [1698] J. Derrac, S. García, F. Herrera. Fuzzy Nearest Neighbor Algorithms: Taxonomy, Experimental analysis and Prospects . Information Sciences 260 (2014) 98-119. doi: 10.1016/j.ins.2013.10.038
  COMPLEMENTARY MATERIAL to the paper: datasets, experimental results and source codes
  
  
• [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
  
  
• [1588] V. López, I. Triguero, C.J. Carmona, S. García, F. Herrera. Addressing Imbalanced Classification with Instance Generation Techniques: IPADE-ID. Neurocomputing 126 (2014) 15-28. doi: 10.1016/j.neucom.2013.01.050
  
• [1642] I. Palomares, L. Martínez, F. Herrera. A Consensus Model to Detect and Manage Non-Cooperative Behaviors in Large Scale Group Decision Making. IEEE Transactions on Fuzzy Systems 22:3 (2014) 516–530. doi: 10.1109/TFUZZ.2013.2262769
  
• [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
  
• [1659] I. Palomares, L. Martínez, F. Herrera. MENTOR: A Graphical Monitoring Tool of Preferences Evolution in Large-Scale Group Decision Making. Knowledge-based Systems 58 (2014) 66–74. doi: 10.1016/j.knosys.2013.07.003
  
• [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
  
  
• [1672] V. López, A. Fernandez, F. Herrera. On the Importance of the Validation Technique for Classification with Imbalanced Datasets: Addressing Covariate Shift when Data is Skewed. Information Sciences 257 (2014) 1-13. doi: 10.1016/j.ins.2013.09.038
  
• [1673] D. Martín, A. Rosete, J. Alcalá-Fdez, F. Herrera. QAR-CIP-NSGA-II: A New Multi-Objective Evolutionary Algorithm to Mine Quantitative Association Rules. Information Sciences 258 (2014) 1-28. doi: 10.1016/j.ins.2013.09.009
  
• [1674] D. Martín, A. Rosete, J. Alcalá-Fdez, F. Herrera. A New Multi-Objective Evolutionary Algorithm for Mining a Reduced Set of Interesting Positive and Negative Quantitative Association Rules. IEEE Transactions on Evolutionary Computation 18:1 (2014) 54-69. doi: 10.1109/TEVC.2013.2285016
  
• [1708] F. Charte Ojeda, A. Rivera, M.J. del Jesus, F. Herrera. LI-MLC: A Label Inference Methodology for Addressing High Dimensionality in the Label Space for Multi-label Classification. IEEE Transactions on Neural Networks and Learning Systems, 25:10 (2014) 1842-1854. doi: 10.1109/TNNLS.2013.2296501
  
• [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
  
• [1706] B. Lacroix, D. Molina, F. Herrera. Region based memetic algorithm for real-parameter optimisation. Information Sciences, 262 (2014) 15-31. doi: 10.1016/j.ins.2013.11.032
  
• [1709] M, Galar, A. Fernandez, E. Barrenechea, F. Herrera. Empowering difficult classes with a Similarity-based aggregation in multi-class classification problems. Information Sciences 264 (2014) 135-157. doi: 10.1016/j.ins.2013.12.053
  
• [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
  
  
• [1732] M.J. Gacto, M. Galende, R. Alcalá, F. Herrera. METSK-HDe: A Multiobjective Evolutionary Algorithm to learn accurate TSK-fuzzy Systems in High-Dimensional and Large-Scale Regression Problems. Information Sciences 276 (2014) 63-79. doi: 10.1016/j.ins.2014.02.047
  
• [1736] D.S. Tarragó, C. Cornelis, R. Bello, F. Herrera. A Multi-Instance Learning Wrapper based on the Rocchio Classifier for Web Index Recommendation. Knowledge-Based Systems 59 (2014) 173-181. doi: 10.1016/j.knosys.2014.01.008
  
• [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
  
• [1741] Nicolás Robinson-García, Daniel Torres-Salinas, Emilio Delgado López-Cózar, F. Herrera. An insight into the importance of national university rankings in an international context: The case of the I-UGR Rankings of Spanish universities. Scientometrics 101 (2014) 1309–1324. doi: 10.1007/s11192-014-1263-1
  
• [1763] R.M. Rodríguez, L. Martínez, V. Torra, Z.S. Xu, F. Herrera. Hesitant Fuzzy Sets: State of the Art and Future Directions. International Journal of Intelligent Systems 29 (2014) 495-524. doi: 10.1002/int.21654
  
• [1764] I. Palomares, F.J. Estrella, L. Martínez, F. Herrera. Consensus under a fuzzy context: Taxonomy, analysis framework AFRYCA and experimental case of study. Information Fusion 20 (2014) 252-271. doi: 10.1016/j.inffus.2014.03.002
  
• [1766] C.J. Carmona, P. González, M.J. del Jesus, F. Herrera. Overview on evolutionary subgroup discovery: analysis of the suitability and potential of the search performed by evolutionary algorithms. WIREs Data Mining Knowledge Discovevery 4 (2014), 87-103. doi: 10.1002/widm.1118
  
• [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
  
• [1773] N. Verbiest, E. Ramentol, C. Cornelis, F. Herrera. Preprocessing Noisy Imbalanced Datasets using SMOTE enhanced with Fuzzy Rough Prototype Selection. Applied Soft Computing 22 (2014) 511-517. doi: 10.1016/j.asoc.2014.05.023
  
• [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.
  
• [1789] F.J. Estrella, M. Espinilla, F. Herrera, L. Martínez. FLINTSTONES: A fuzzy linguistic decision tools enhancement suite based on the 2-tuple linguistic model and extensions. Information Sciences 280 (2014) 152-170. doi: 10.1016/j.ins.2014.04.049
  
• [1794] J. Derrac, S. García, S. Hui, P. N. Suganthan, F. Herrera. Analyzing convergence performance of evolutionary algorithms: A statistical approach. Information Sciences 289 (2014) 41-58. doi: 10.1016/j.ins.2014.06.009
  COMPLEMENTARY MATERIAL to the paper
  
  
• [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
  
• [1816] M. Fazzolari, R. Alcalá, F. Herrera. A Multi-Objective Evolutionary Method for Learning Granularities based on Fuzzy Discretization to Improve the Accuracy-Complexity trade-off of Fuzzy Rule-Based Classification Systems: D-MOFARC Algorithm. Applied Soft Computing 24 (2014) 470-481.
  
• [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.
  
• [1895] F. Herrera. Big data: Procesando los datos en la sociedad digital. Revista de Física 28:4 (2014) 40-44.
  
• [2059] B. Krawczyk, M. Wozniak, F. Herrera. Weighted one-class classification for different types of minority class examples in imbalanced data. Computational Intelligence and Data Mining (CIDM), 2014 IEEE Symposium on Orlando, FL. 9-12 Dec. 2014. 337-344. doi: 10.1109/CIDM.2014.7008687
  
• [2432] S. García, J. Derrac, S. Ramírez-Gallego, F. Herrera. On the statistical analysis of the parameters’ trend in a machine learning algorithm. Progress in Artificial Intelligence 3:1 (2014) 51-53. doi: 10.1007/s13748-014-0043-8
  

2013 (15)

• [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
  
  
• [1521] M. Fazzolari, R. Alcalá, Y. Nojima, H. Ishibuchi, F. Herrera. A Review of the Application of Multi-Objective Evolutionary Fuzzy Systems: Current Status and Further Directions. IEEE Transactions on Fuzzy Systems, 21:1 (2013) 45-65.
  COMPLEMENTARY MATERIAL to the paper: links to the papers with doi, new contributions, etc
  
  
• [1537] J. Derrac, N. Verbiest, S. García, C. Cornelis, F. Herrera. On the use of Evolutionary Feature Selection for Improving Fuzzy Rough Set Based Prototype Selection. Soft Computing 17:2 (2013) 223-238. doi: 10.1007/s00500-012-0888-3
  
• [1554] V. López, A. Fernandez, M.J. del Jesus, F. Herrera. A Hierarchical Genetic Fuzzy System Based On Genetic Programming for Addressing Classification with Highly Imbalanced and Borderline Data-sets. Knowledge-Based Systems 38 (2013) 85-104. doi: 10.1016/j.knosys.2012.08.025
  
• [1570] D. Torres-Salinas, N. Robinson-García, E. Jiménez-Contreras, F. Herrera, E. Delgado López-Cózar. On the Use of Biplot Analysis for Multivariate Bibliometric and Scientific Indicators. Journal of the American Society for Information Science and Technology 64:7 (2013) 1468-1479.
  
• [1587] J. Sanz, A. Fernandez, H. Bustince, F. Herrera. IVTURS: a linguistic fuzzy rule-based classification system based on a new Interval-Valued fuzzy reasoning method with TUning and Rule Selection. IEEE Transactions on Fuzzy Systems 21:3 (2013) 399-411. doi: 10.1109/TFUZZ.2013.2243153
  
• [1594] A. Fernandez, V. López, M. Galar, M.J. del Jesus, F. Herrera. Analysing the Classification of Imbalanced Data-sets with Multiple Classes: Binarization Techniques and Ad-Hoc Approaches. Knowledge-Based Systems 42 (2013) 97-110. doi: 10.1016/j.knosys.2013.01.018
  
• [1634] N. Verbiest, C. Cornelis, F. Herrera. FRPS: A Fuzzy Rough Prototype Selection method. Pattern Recognition 46:10 (2013) 2770-2782. doi: 10.1016/j.patcog.2013.03.004
  
• [1635] R.M. Rodriguez, L. Martínez, F. Herrera. A Group Decision Making Model Dealing with Comparative Linguistic Expressions based on Hesitant Fuzzy Linguistic Term Sets. Information Sciences 241 (2013) 28–42. doi: 10.1016/j.ins.2013.04.006
  
• [1641] M. Galar, A. Fernandez, E. Barrenechea, F. Herrera. EUSBoost: Enhancing Ensembles for Highly Imbalanced Data-sets by Evolutionary Undersampling. Pattern Recognition 46:12 (2013) 3460–3471. doi: j.patcog.2013.05.006
  
• [1643] M. Galar, A. Fernandez, E. Barrenechea, H. Bustince, F. Herrera. Dynamic Classifier Selection for One-vs-One Strategy: Avoiding Non-Competent Classifiers. Pattern Recognition 46:12 (2013) 3412–3424. doi: j.patcog.2013.04.018
  
• [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
  
  
• [1657] V. López, A. Fernandez, S. García, V. Palade, F. Herrera. An Insight into Classification with Imbalanced Data: Empirical Results and Current Trends on Using Data Intrinsic Characteristics. Information Sciences 250 (2013) 113-141. doi: 10.1016/j.ins.2013.07.007
  COMPLEMENTARY MATERIAL to the paper
  
  
• [1680] M. Fazzolari, B. Giglio, R. Alcalá, F. Marcelloni, F. Herrera. A study on the application of instance selection techniques in genetic fuzzy rule-based classification systems: Accuracy-complexity trade-off. Knowledge-Based Systems 54 (2013) 32-41. doi: 10.1016/j.knosys.2013.07.011
  

2012 (28)

• [1425] J. G. Moreno-Torres, T. R. Raeder, R. Aláiz-Rodríguez, N. V. Chawla, F. Herrera. A unifying view on dataset shift in classification. Pattern Recognition 45:1 (2012) 521-530. doi: 10.1016/j.patcog.2011.06.019
  
• [1241] E. Pérez, M. Posada, F. Herrera. Analysis of New Niching Genetic Algorithms for Finding Multiple Solutions in the Job Shop Scheduling. Journal of Intelligent Manufacturing 23:3 (2012) 341-356. doi: 10.1007/s10845-010-0385-4
  
• [1328] M.J. Gacto, R. Alcalá, F. Herrera. A Multi-Objective Evolutionary Algorithm for an Effective Tuning of Fuzzy Logic Controllers in Heating, Ventilating and Air Conditioning Systems. Applied Intelligence 36:2 (2012) 330-347. doi: 10.1007/s10489-010-0264-x
  
• [1336] F. Chávez, F. Fernández, R. Alcalá, J. Alcalá-Fdez, G. Olague, F. Herrera. Hybrid Laser Pointer Detection Algorithm Based on Template Matching and Fuzzy Rule-Based Systems for Domotic Control in Real Home Enviroments. Applied Intelligence 36:2 (2012) 407-423. doi: 10.1007/s10489-010-0268-6
  
• [1365] I. Triguero, J. Derrac, S. García, F. Herrera. A Taxonomy and Experimental Study on Prototype Generation for Nearest Neighbor Classification. IEEE Transactions on Systems, Man, and Cybernetics--Part C: Applications and Reviews 42 (1) (2012) 86-100. doi: 10.1109/TSMCC.2010.2103939
  COMPLEMENTARY MATERIAL to the paper: datasets, experimental results and source codes
  
  
• [1372] S. García, J. Derrac, I. Triguero, C.J. Carmona, F. Herrera. Evolutionary-Based Selection of Generalized Instances for Imbalanced Classification. Knowledge Based Systems 25:1 (2012) 3-12. doi: 10.1016/j.knosys.2011.01.012
  
• [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
  
  
• [1409] S. García, J. Derrac, J.R. Cano, F. Herrera. Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study. IEEE Transactions on Pattern Analysis and Machine Intelligence 34:3 (2012) 417-435. doi: 10.1109/TPAMI.2011.142
  COMPLEMENTARY MATERIAL to the paper: datasets, experimental results and source codes
  
  
• [1414] J. Marín, D. Molina, F. Herrera. Modeling Dynamics of a Real-coded CHC Algorithm in Terms of Dynamical Probability Distributions. Soft Computing 16:2 (2012) 331-351. doi: 10.1007/s00500-011-0745-9
  
• [1422] M. Galar, A. Fernandez, E. Barrenechea, H. Bustince, F. Herrera. A Review on Ensembles for Class Imbalance Problem: Bagging, Boosting and Hybrid Based Approaches. IEEE Transactions on System, Man and Cybernetics - Part C: Applications and Reviews 42:4 (2012) 463-484. doi: 10.1109/TSMCC.2011.2161285
  
• [1428] J. Derrac, C. Cornelis, S. García, F. Herrera. Enhancing Evolutionary Instance Selection Algorithms by means of Fuzzy Rough Set based Feature Selection. Information Sciences 186:1 (2012) 73-92. doi: 10.1016/j.ins.2011.09.027
  
• [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
  
• [1431] R.M. Rodríguez, L. Martínez, F. Herrera. Hesitant Fuzzy Linguistic Terms Sets for Decision Making. IEEE Transactions on Fuzzy Systems 20:1 (2012) 109-119. doi: 10.1109/TFUZZ.2011.2170076
  
• [1432] M.J. Cobo, A.G. López-Herrera, F. Herrera, E. Herrera-Viedma. A Note on the ITS Topic Evolution in the Period 2000–2009 at T-ITS. IEEE Transactions on Intelligent Transportation Systems, 13:1, pp. 413-420 (2012). doi: 10.1109/TITS.2011.2167968
  
• [1433] Amilkar Puris, Rafael Bello, D. Molina, F. Herrera. Variable mesh optimization for continuous optimization problems. Soft Computing - A Fusion of Foundations, Methodologies and Applications (2012), 16(3), 511-525. doi: 10.1007/s00500-011-0753-9
  
• [1434] E. Ramentol, Y. Caballero, R. Bello, F. Herrera. SMOTE-RSB*: A Hybrid Preprocessing Approach based on Oversampling and Undersampling for High Imbalanced Data-Sets using SMOTE and Rough Sets Theory. Knowledge and Information Systems 33:2 (2012) 245-265. doi: 10.1007/s10115-011-0465-6
  
• [1442] H. Bustince, M. Pagola, R. Mesiar, E. Hullermeier, F. Herrera. Grouping, Overlap and Generalized Bi-Entropic Functions for Fuzzy Modeling of Pairwise Comparisons. IEEE Transactions on Fuzzy Systems 20:3 (2012) 405-415. doi: 10.1109/TFUZZ.2011.2173581
  
• [1485] V. López, A. Fernandez, J. G. Moreno-Torres, F. Herrera. Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics. Expert Systems with Applications 39:7 (2012) 6585-6608. doi: 10.1016/j.eswa.2011.12.043
  
• [1512] M.J. Cobo, A.G. López-Herrera, E. Herrera-Viedma, F. Herrera. SciMAT: A new Science Mapping Analysis Software Tool. Journal of the American Society for Information Science and Technology 63:8, pp. 1609-1630 (2012) . SciMAT's website here. doi: 10.1002/asi.22688
  
• [1514] J. Derrac, I. Triguero, S. García, F. Herrera. Integrating Instance Selection, Instance Weighting and Feature Weighting for Nearest Neighbor Classifiers by Co-evolutionary Algorithms. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 42:5 (2012) 1383-1397. doi: 10.1109/TSMCB.2012.2191953
  COMPLEMENTARY MATERIAL to the paper: datasets, experimental results and source codes
  
  
• [1518] J. A. García, R. Rodriguez-Sánchez, J. Fdez-Valdivia, D. Torres-Salinas, F. Herrera. Ranking of research output of universities on the basis of the multidimensional prestige of influential fields: Spanish universities as a case of study. Scientometrics 93:3 (2012) 681-698. doi: 10.1007/s11192-012-0740-7
  
• [1519] L. Martínez, F. Herrera. An overview on the 2-tuple linguistic model for Computing with Words in Decision Making: Extensions, applications and challenges. Information Sciences 207 (2012) 1-18. doi: 10.1016/j.ins.2012.04.025
  
• [1522] J. Sanz, A. Fernandez, H. Bustince, F. Herrera. IIVFDT: Ignorance Functions based Interval-Valued Fuzzy Decision Tree with Genetic Tuning. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20:2 (2012) 1-30. doi: 10.1142/S0218488512500195
  
• [1523] P. Villar, A. Fernandez, R.A. Carrasco, F. Herrera. Feature Selection and Granularity Learning in Genetic Fuzzy Rule-Based Classication Systems for Highly Imbalanced Data-Sets. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20:3 (2012) 369-397. doi: S0218488512500195
  
• [1530] I. Triguero, J. Derrac, S. García, F. Herrera. Integrating a Differential Evolution Feature Weighting scheme into Prototype Generation. Neurocomputing 97 (2012) 332-343. doi: 10.1016/j.neucom.2012.06.009
  
• [1538] J. G. Moreno-Torres, José A. Sáez, F. Herrera. Study on the Impact of Partition-Induced Dataset Shift on k-fold Cross-Validation. IEEE Transactions on Neural Networks and Learning Systems 23:8 (2012) 1304-1312. doi: 10.1109/TNNLS.2012.2199516
  COMPLEMENTARY MATERIAL to the paper
  
  
• [1542] D. Docampo, F. Herrera, T. Luque-Martínez, D. Torres-Salinas. Efecto de la agregación de universidades españolas en el Ranking de Shanghai (ARWU): caso de las comunidades autónomas y los campus de excelencia. El profesional de la información 21:4 (2012) 428-432. doi: 10.3145/epi.2012.jul.16
  

2011 (19)

• [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
  
  
• [1321] M.J. Cobo, A.G. López-Herrera, E. Herrera-Viedma, F. Herrera. An Approach for Detecting, Quantifying, and Visualizing the Evolution of a Research Field: A Practical Application to the Fuzzy Sets Theory Field. Journal of Informetrics 5:1, pp. 146-166 (2011). doi: 10.1016/j.joi.2010.10.002
  
• [1323] D. Molina, M. Lozano, A.M. Sánchez, F. Herrera. Memetic Algorithms Based on Local Search Chains for Large Scale Continuous Optimisation Problems: MA-SSW-Chains . Soft Computing, 15 (2011) 2201-2220. doi: 10.1007/s00500-010-0647-2
  
• [1324] F. Herrera, C.J. Carmona, P. González and M.J. del Jesus. An overview on Subgroup Discovery: Foundations and Applications . Knowledge and Information Systems 29:3 (2011) 495-525. doi: 10.1007/s10115-010-0356-2
  
• [1327] I. Triguero, S. García, F. Herrera. Differential Evolution for Optimizing the Positioning of Prototypes in Nearest Neighbor Classification. Pattern Recognition 44 (4) (2011) 901-916. doi: 10.1016/j.patcog.2010.10.020
  
• [1329] R. Alcalá, Y. Nojima, F. Herrera, H. Ishibuchi. Multiobjective Genetic Fuzzy Rule Selection of Single Granularity-Based Fuzzy Classification Rules and its Interaction with the Lateral Tuning of Membership Functions. Soft Computing 15:12 (2011) 2303-2318. doi: 10.1007/s00500-010-0671-2
  
• [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
  
• [1368] D. Torres-Salinas, E. Delgado-López-Cózar, J G Moreno-Torres, F. Herrera. Rankings ISI de las universidades españolas según campos científicos: Descripción y resultados. El Profesional de la Información 20:1 (2011) 111-122.
  
• [1371] M. Galar, A. Fernandez, E. Barrenechea, H. Bustince, F. Herrera. An Overview of Ensemble Methods for Binary Classifiers in Multi-class Problems: Experimental Study on One-vs-One and One-vs-All Schemes. Pattern Recognition 44:8 (2011) 1761-1776. doi: 10.1016/j.patcog.2011.01.017
  COMPLEMENTARY MATERIAL to the paper: dataset partitions, results, figures, etc
  
  
• [1373] J. Sanz, A. Fernandez, H. Bustince, F. Herrera. A Genetic Tuning to Improve the Performance of Fuzzy Rule-Based Classification Systems with Interval-Valued Fuzzy Sets: Degree of Ignorance and Lateral Position 52:6 (2011) 751-766. International Journal of Approximate Reasoning. doi: 10.1016/j.ijar.2011.01.011
  
• [1374] J. Derrac, S. García, D. Molina, F. Herrera. A Practical Tutorial on the Use of Nonparametric Statistical Tests as a Methodology for Comparing Evolutionary and Swarm Intelligence Algorithms. Swarm and Evolutionary Computation 1:1 (2011) 3-18. doi: 10.1016/j.swevo.2011.02.002
  COMPLEMENTARY MATERIAL to the paper: Software and tests description
  
  
• [1376] M.J. Cobo, A.G. López-Herrera, E. Herrera-Viedma, F. Herrera. Science Mapping Software Tools: Review, Analysis and Cooperative Study among Tools. Journal of the American Society for Information Science and Technology, 62:7, pp. 1382–1402 (2011). doi: 10.1002/asi.21525
  
• [1380] R. Alcalá, M.J. Gacto, F. Herrera. A Fast and Scalable Multi-Objective Genetic Fuzzy System for Linguistic Fuzzy Modeling in High-Dimensional Regression Problems. IEEE Transactions on Fuzzy Systems 19:4 (2011) 666-681. doi: 10.1109/TFUZZ.2011.2131657
  COMPLEMENTARY MATERIAL to the paper: dataset partitions, results, figures, etc
  
  
• [1383] M.J. Gacto, R. Alcalá, F. Herrera. Interpretability of Linguistic Fuzzy Rule-Based Systems: An Overview of Interpretability Measures. Information Sciences, 181:20 (2011) 4340–4360.
  COMPLEMENTARY MATERIAL to the paper: links to the papers with doi, new contributions, etc
  
  
• [1402] J. Alcalá-Fdez, R. Alcalá, F. Herrera. A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems with Genetic Rule Selection and Lateral Tuning. IEEE Transactions on Fuzzy Systems 19:5 (2011) 857-872. doi: 10.1109/TFUZZ.2011.2147794
  
• [1412] D. Torres-Salinas, J G Moreno-Torres, E. Delgado-López-Cózar, F. Herrera. A methodology for Institution-Field ranking based on a bidimensional analysis: the IFQ2A index. Scientometrics 88:3 (2011) 771-786.
  
• [1470] D. Torres-Salinas, J G Moreno-Torres, N. Robinson-García, E. Delgado-López-Cózar, F. Herrera. Rankings ISI de las universidades españolas según campos y disciplinas científicas (2ª ed. 2011). El Profesional de la Información 20:6 (2011) 701-711.
  
• [1821] M. Lozano, D. Molina, F. Herrera. Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. Soft Computing 15:11 (2011) 2085-2087. doi: 10.1007/s00500-010-0639-2
  

2010 (24)

• [0897] S. Alonso, F.J. Cabrerizo, E. Herrera-Viedma, F. Herrera. hg-index: A New Index to Characterize the Scientific Output of Researchers Based on the h- and g- Indices. Scientometrics 82:2 (2010) 391-400. doi: 10.1007/s11192-009-0047-5
  
• [0849] D. Molina, M. Lozano, C. García-Martínez, F. Herrera. Memetic Algorithms for Continuous Optimization Based on Local Search Chains. Evolutionary Computation, 18:1 (2010) 27-63. doi: 10.1162/evco.2010.18.1.18102
  
• [1226] J. Derrac, S. García, F. Herrera. IFS-CoCo: Instance and Feature Selection based on Cooperative Coevolution with Nearest Neighbor Rule. Pattern Recognition 43:6 (2010) 2082-2105. doi: 10.1016/j.patcog.2009.12.012
  
• [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
  
• [1065] F.J. Cabrerizo, S. Alonso, E. Herrera-Viedma, F. Herrera. q²-Index: Quantitative and Qualitative Evaluation Based on the Number and Impact of Papers in the Hirsch Core. Journal of Informetrics 4:1 (2010) 23-28. doi: 10.1016/j.joi.2009.06.005
  
• [1092] P. Espejo, S. Ventura, F. Herrera. A Survey on the Application of Genetic Programming to Classification. IEEE Transactions on Systems, Man, and Cybernetics--Part C: Applications and Reviews 40:2 (2010) 121-144. doi: 10.1109/TSMCC.2009.2033566
  
• [1100] J. Derrac, S. García, F. Herrera. A Survey on Evolutionary Instance Selection and Generation. International Journal of Applied Metaheuristic Computing 1:1 (2010) 60-92. doi: 10.4018/IJAMC.2010010104
  
• [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
  
  
• [1114] A.G. López-Herrera, M.J. Cobo, E. Herrera-Viedma, F. Herrera. A Bibliometric Study about the Research Based on Hybridating the Fuzzy Logic Field and the Other Computational Intelligent Techniques: A Visual Approach. Internacional Journal of Hybrid Intelligent Systems 17:7 (2010) 17–32. doi: 10.3233/HIS-2010-0102
  
• [1186] J. Alcalá-Fdez, N. Flugy-Pape, A. Bonarini, F. Herrera. Analysis of the Effectiveness of the Genetic Algorithms based on Extraction of Association Rules. Fundamenta Informaticae 98:1 (2010) 1001-1014.
  
• [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
  
  
• [1227] A. Fernandez, M.J. del Jesus, F. Herrera. On the 2-Tuples Based Genetic Tuning Performance for Fuzzy Rule Based Classification Systems in Imbalanced Data-Sets. Information Sciences 180:8 (2010) 1268-1291. doi: 10.1016/j.ins.2009.12.014
  COMPLEMENTARY MATERIAL to the paper here: dataset partitions, results, figures, etc
  
  
• [1228] M.J. Gacto, R. Alcalá, F. Herrera. Integration of an Index to Preserve the Semantic Interpretability in the Multi-Objective Evolutionary Rule Selection and Tuning of Linguistic Fuzzy Systems. IEEE Transactions on Fuzzy Systems 18:3 (2010) 515-531. doi: 10.1109/TFUZZ.2010.2041008
  
• [1229] F.J. Berlanga, A.J. Rivera, M.J. del Jesus, F. Herrera. GP-COACH: Genetic Programming based learning of COmpact and ACcurate fuzzy rule based classification systems for high dimensional problems. Information Sciences 180:8 (2010) 1183-1200. doi: 10.1016/j.ins.2009.12.020
  
• [1242] A. Puris, R. Bello and F. Herrera. Analysis of the efficacy of a Two-Stage methodology for Ant Colony Optimization: Case of study with TSP and QAP. Expert Systems with Applications 37:7 (2010) 5443-5453. doi: 10.1016/j.eswa.2010.02.069
  
• [1259] S. Alonso, F.J. Cabrerizo, E. Herrera-Viedma, F. Herrera. WoS Query Partitioner: A tool to retrieve very large numbers of items from the Web of Science using different source based partitioning approaches. Journal of the American Society for Information Science and Technology 61:8 (2010) 1582-1597. doi: 10.1002/asi.21360
  SOFTWARE associated to the paper
  
  
• [1273] A. Fernandez, M. Calderón, E. Barrenechea, H. Bustince, F. Herrera. Solving Multi-Class Problems with Linguistic Fuzzy Rule Based Classification Systems Based on Pairwise Learning and Preference Relations. Fuzzy Sets and Systems 161:23 (2010) 3064-3080. doi: 10.1016/j.fss.2010.05.016
  
• [1278] J.A. Sanz, A. Fernandez, H. Bustince, F. Herrera. Improving the Performance of Fuzzy Rule-Based Classification Systems with Interval-Valued Fuzzy Sets and Genetic Amplitude Tuning. Information Sciences 180:19 (2010) 3674-3685. doi: 10.1016/j.ins.2010.06.018
  
• [1285] J. Derrac, S. García, F. Herrera. Stratified Prototype Selection based on a Steady-State Memetic Algorithm: A Study of scalability. Memetic Computing 2:3 (2010) 183-199. doi: 10.1007/s12293-010-0048-1
  
• [1293] C. Carmona, P. González, M.J. del Jesus, F. Herrera. NMEEF-SD: Non-dominated Multi-objective Evolutionary algorithm for Extracting Fuzzy rules in Subgroup Discovery. IEEE Transactions on Fuzzy Systems Issue 18:5 (2010) 958-970. doi: 10.1109/TFUZZ.2010.2060200
  
• [1295] S. Alonso, E. Herrera-Viedma, F. Chiclana , F. Herrera. A Web Based Consensus Support System for Group Decision Making Problems and Incomplete Preferences. Information Sciences, 180:23 (2010), 4477-4495. doi: 10.1016/j.ins.2010.08.005
  
• [1316] I. Triguero, S. García, F. Herrera. IPADE: Iterative Prototype Adjustment for Nearest Neighbor Classification. IEEE Transactions on Neural Networks 21 (12) (2010) 1984-1990. doi: 10.1109/TNN.2010.2087415
  COMPLEMENTARY MATERIAL to the paper: datasets, experimental results and source codes
  
  
• [1320] L. Martinez, D. Ruan, F. Herrera. Computing with Words in Decision support Systems: An overview on Models and Applications. International Journal of Computational Intelligence Systems 3:4 (2010) 382-395.
  

2009 (26)

• [0758] J. Alcalá-Fdez, L. Sánchez, S. García, M.J. del Jesus, S. Ventura, J.M. Garrell, J. Otero, C. Romero, J. Bacardit, V.M. Rivas, J.C. Fernández, F. Herrera. KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems. Soft Computing 13:3 (2009) 307-318. doi: 10.1007/s00500-008-0323-y
  SOFTWARE associated to the paper
  
  
• [0591] P.J. Sánchez, L. Martínez, C. García-Martínez, F. Herrera, E. Herrera-Viedma. A fuzzy model to evaluate the suitability of installing an enterprise resource planning system. Information Sciences 179 (2009) 2333-2341. doi: 10.1016/j.ins.2008.12.020
  
• [0671] R. Alcalá, J. Alcalá-Fdez, M.J. Gacto, F. Herrera. Improving Fuzzy Logic Controllers Obtained by Experts: A Case Study in HVAC Systems. Applied Intelligence 31:1 (2009) 15-30. doi: 10.1007/s10489-007-0107-6
  
• [0769] C. Romero, P. González, S. Ventura, M.J. del Jesus, F. Herrera. Evolutionary algorithms for subgroup discovery in e-learning: A practical application using Moodle data. Expert Systems With Applications 36 (2009) 1632-1644. doi: 10.1016/j.eswa.2007.11.026
  
• [0976] A.G. López-Herrera, E. Herrera-Viedma, F. Herrera. Applying Multi-objective Evolutionary Algorithms to the Automatic Learning of Extended Boolean Queries in Fuzzy Ordinal Linguistic Information Retrieval Systems. Fuzzy Sets and Systems, 160 (2009) 2192 – 2205. doi: 10.1016/j.fss.2009.02.013
  
• [0838] J. Alcalá-Fdez, R. Alcalá, M.J. Gacto, F. Herrera. Learning the Membership Function Contexts for Mining Fuzzy Association Rules by Using Genetic Algorithms. Fuzzy Sets and Systems 160:7 (2009) 905-921. doi: 10.1016/j.fss.2008.05.012
  
• [0837] M.J. Gacto, R. Alcalá, F. Herrera. Adaptation and Application of Multi-Objective Evolutionary Algorithms for Rule Reduction and Parameter Tuning of Fuzzy Rule-Based Systems. Soft Computing 13:5 (2009) 419-436. doi: 10.1007/s00500-008-0359-z
  
• [0834] S. García, D. Molina, M. Lozano, F. Herrera. A Study on the Use of Non-Parametric Tests for Analyzing the Evolutionary Algorithms' Behaviour: A Case Study on the CEC'2005 Special Session on Real Parameter Optimization. Journal of Heuristics 15 (2009) 617-644. doi: 10.1007/s10732-008-9080-4
  COMPLEMENTARY MATERIAL to the paper: Software and tests description
  
  
• [0826] S. García, F. Herrera. Evolutionary Under-Sampling for Classification with Imbalanced Data Sets: Proposals and Taxonomy. Evolutionary Computation 17:3 (2009) 275-306.
  
• [0855] S. Alonso, F.J. Cabrerizo, F. Chiclana, F. Herrera, E. Herrera-Viedma. Group Decision-Making with Incomplete Fuzzy Linguistic Preference Relations. International Journal of Intelligent Systems 24:2 (2009) 201-222. doi: 10.1002/int.20332
  
• [0875] S. García, J.R. Cano, E. Bernadó-Mansilla, F. Herrera. Diagnose of Effective Evolutionary Prototype Selection using an Overlapping Measure. International Journal of Pattern Recognition and Artificial Intelligence 23:8 (2009) 1527-1548.
  
• [0885] F. Chiclana, E. Herrera-Viedma, S. Alonso, F. Herrera. Cardinal Consistency of Reciprocal Preference Relations: A Characterization of Multiplicative Transitivity. IEEE Transactions on Fuzzy Systems 17:1 (2009) 14-23. doi: 10.1109/TFUZZ.2008.2008028
  
• [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
  
  
• [0894] A.M. Sánchez, M. Lozano, P. Villar, F. Herrera. Hybrid Crossover Operators with Multiple Descendents for Real-Coded Genetic Algorithms: Combining Neighborhood-based Crossover Operators. International Journal of Intelligent Systems 24:5 (2009) 540-567. doi: 10.1002/int.20348
  
• [0896] A. Fernandez, M.J. del Jesus, F. Herrera. Hierarchical Fuzzy Rule Based Classification Systems with Genetic Rule Selection for Imbalanced Data-Sets. International Journal of Approximate Reasoning 50 (2009) 561-577. doi: 10.1016/j.ijar.2008.11.004
  COMPLEMENTARY MATERIAL to the paper: dataset partitions, results, figures, etc
  
  
• [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
  
  
• [0947] A.G. López-Herrera, E. Herrera-Viedma, F. Herrera. A Study of the Use of Multi-Objective Evolutionary Algorithms to Learn Boolean Queries: A Comparative Study. Journal of the American Society for Information Science and Technology 60:6 (2009) 1192 - 1207. doi: 10.1002/asi.21060
  
• [0949] S. Alonso, E. Herrera-Viedma, F. Chiclana, F. Herrera. Individual and Social Strategies to Deal With Ignorance Situations in Multi-Person Decision Making. International Journal of Information Technology and Decision Making 8:2 (2009) 313-333. doi: 10.1142/S0219622009003417
  
• [1018] A. Fernandez, F. Herrera, M.J. del Jesus. On the Influence of an Adaptive Inference System in Fuzzy Rule Based Classification Systems for Imbalanced Data-Sets. Expert Systems With Applications 36:6 (2009) 9805-9812. doi: 10.1016/j.eswa.2009.02.048
  
• [1046] S. Alonso. F.J. Cabrerizo, E. Herrera-Viedma, F. Herrera. h-index: A Review Focused in its Variants, Computation and Standardization for Different Scientific Fields. Journal of Informetrics 3:4 (2009) 273-289. doi: 10.1016/j.joi.2009.04.001
  
• [1047] S. García, A. Fernandez, F. Herrera. Enhancing the Effectiveness and Interpretability of Decision Tree and Rule Induction Classifiers with Evolutionary Training Set Selection over Imbalanced Problems. Applied Soft Computing 9 (2009) 1304-1314. doi: 10.1016/j.asoc.2009.04.004
  
• [1054] R. Alcalá, P. Ducange, F. Herrera, B. Lazzerini, F. Marcelloni. A Multi-Objective Evolutionary Approach to Concurrently Learn Rule and Data Bases of Linguistic Fuzzy Rule-Based Systems. IEEE Transactions on Fuzzy Systems 17:5 (2009) 1106-1122. doi: 10.1109/TFUZZ.2009.2023113
  
• [1069] F. Herrera, S. Alonso, F. Chiclana, E. Herrera-Viedma. Computing With Words in Decision Making: Foundations, Trends and Prospects. Fuzzy Optimization and Decision Making 8:4 (2009) 337-364. doi: 10.1007/s10700-009-9065-2
  
• [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
  
• [1096] F. Herrera, E. Herrera-Viedma, S. Alonso, F.J. Cabrerizo. Agregación de índices bibliométricos para evaluar la producción científica de los investigadores. El Profesional de la Información 18:5 (2009) 559-561. doi: 10.3145/epi.2009.sep.11
  
• [1115] A.G. López-Herrera, M.J. Cobo, E. Herrera-Viedma, F. Herrera, R. Bailón, E. Jiménez-Contreras. Visualization and Evolution of the Scientific Structure of Fuzzy Sets Research in Spain. Information Research 14:4, paper 421 (2009), Available online.
  

2008 (14)

• [0341] M. Lozano, F. Herrera, J.R. Cano. Replacement Strategies to Preserve Useful Diversity in Steady-State Genetic Algorithms. Information Sciences 178:23 (2008) 4421-4433. doi: 10.1016/j.ins.2008.07.031
  
• [0446] S. Alonso, F. Chiclana, F. Herrera, E. Herrera-Viedma, J. Alcalá-Fdez, C. Porcel. A Consistency-Based Procedure to Estimate Missing Pairwise Preference Values. International Journal of Intelligent Systems 23:2 (2008) 155-175. doi: 10.1002/int.20262
  
• [0526] C. García-Martínez, M. Lozano, F. Herrera, D. Molina, A.M. Sánchez. Global and Local Real-Coded Genetic Algorithms Based on Parent-Centric Crossover Operators. European Journal of Operational Research 185 (2008) 1088-1113. doi: 10.1016/j.ejor.2006.06.043
  
• [0596] F. Herrera, E. Herrera-Viedma, L. Martínez. A Fuzzy Linguistic Methodology To Deal With Unbalanced Linguistic Term Sets. IEEE Transactions on Fuzzy Systems 16:2 (2008) 354-370. doi: 10.1109/TFUZZ.2007.896353
  
• [0669] A.M. Sánchez, M. Lozano, C. García-Martínez, D. Molina, F. Herrera. Real-Parameter Crossover Operators with Multiple Descendents: An Experimental Study. International Journal of Intelligent Systems 23:2 (2008) 246-268. doi: 10.1002/int.20258
  
• [0721] J.R. Cano, F. Herrera, M. Lozano, S. García. Making CN2-SD Subgroup Discovery Algorithm scalable to Large Size Data Sets using Instance Selection. Expert Systems with Applications 35 (2008) 1949-1965. doi: 10.1016/j.eswa.2007.08.083
  
• [0730] S. Alonso, F.J. Cabrerizo, F. Chiclana, F. Herrera and E. Herrera-Viedma. An Interactive Decision Support System Based on Consistency Criteria. Journal of Multiple-Valued Logic & Soft Computing 14:3-5 (2008) 371-385.
  
• [0760] F. Herrera. Genetic Fuzzy Systems: Taxonomy, Current Research Trends and Prospects. Evolutionary Intelligence 1 (2008) 27-46. doi: 10.1007/s12065-007-0001-5
  
• [0768] R. Romero Zaliz, C. Rubio-Escudero, O. Cordón, J.P. Cobb, F. Herrera, I. Zwir. A multi-objective evolutionary conceptual clustering methodology for gene annotation within structural databases: A case of study on the Gene Ontology database. IEEE Transactions on Evolutionary Computation, 12:6 (2008) 679-701. doi: 10.1109/TEVC.2008.915995
  
• [0772] A. Fernandez, S. García, M.J. del Jesus, F. Herrera. A Study of the Behaviour of Linguistic Fuzzy Rule Based Classification Systems in the Framework of Imbalanced Data Sets. Fuzzy Sets and Systems, 159:18 (2008) 2378-2398. doi: 10.1016/j.fss.2007.12.023
  
• [0847] J.R. Cano, S. García, F. Herrera. Subgroup Discovery in Large Size Data Sets Preprocessed Using Stratified Instance Selection for Increasing the Presence of Minority Classes. Pattern Recognition Letters 29 (2008) 2156-2164. doi: 10.1016/j.patrec.2008.08.001
  
• [0827] S. García, J.R. Cano, F. Herrera. A Memetic Algorithm for Evolutionary Prototype Selection: A Scaling Up Approach. Pattern Recognition 41:8 (2008) 2693-2709. doi: 10.1016/j.patcog.2008.02.006
  
• [0854] F. Chiclana, E. Herrera-Viedma, S. Alonso, F. Herrera. A Note on the Estimation of Missing Pairwise Preference Values: A Uninorm Consistency based Method. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16:2 Supp (2008) 19-32. doi: 10.1142/S0218488508005467
  
• [0882] S. García, F. Herrera. An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons. Journal of Machine Learning Research 9 (2008) 2677-2694.
  COMPLEMENTARY MATERIAL to the paper: Software and tests description
  
  

2007 (13)

• [0344] R. Alcalá, J. Alcalá-Fdez, J. Casillas, O. Cordón, F. Herrera. Local Identification of Prototypes for Genetic Learning of Accurate TSK Fuzzy Rule-Based Systems. International Journal of Intelligent Systems 22:9 (2007) 909-941. doi: 10.1002/int.20232
  
• [0346] J. Alcalá-Fdez, F. Herrera, F. Márquez, A. Peregrín. Increasing Fuzzy Rules Cooperation Based on Evolutionary Adaptive Inference Systems. International Journal of Intelligent Systems 22:9 (2007) 1035-1064. doi: 10.1002/int.20237
  
• [0651] R. Alcalá, M.J. Gacto, F. Herrera, J. Alcalá-Fdez. A Multi-objective Genetic Algorithm for Tuning and Rule Selection to Obtain Accurate and Compact Linguistic Fuzzy Rule-Based Systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 15:5 (2007) 539–557. doi: 10.1142/S0218488507004868
  
• [0427] E. Herrera-Viedma, F. Chiclana, F. Herrera, S. Alonso. Group Decision-Making Model with Incomplete Fuzzy Preference Relations Based on Additive Consistency. IEEE Transactions on Systems, Man and Cybernetics, Part B, Cybernetics, 37:1 (2007) 176-189. doi: 10.1109/TSMCB.2006.875872
  
• [0588] E. Herrera-Viedma, S. Alonso, F. Chiclana, F. Herrera. A Consensus Model for Group Decision Making with Incomplete Fuzzy Preference Relations. IEEE Transactions on Fuzzy Systems 15:5 (2007) 863-877. doi: 10.1109/TFUZZ.2006.889952
  
• [0543] J.R. Cano, F. Herrera, M. Lozano. Evolutionary Stratified Training Set Selection for Extracting Classification Rules with Trade-off Precision-Interpretability. Data and Knowledge Engineering 60 (2007) 90-108. doi: 10.1016/j.datak.2006.01.008
  
• [0556] R. Alcalá, J. Alcalá-Fdez, M.J. Gacto, F. Herrera. Rule Base Reduction and Genetic Tuning of Fuzzy Systems based on the Linguistic 3-Tuples Representation. Soft Computing 11:5 (2007) 401-419. doi: 10.1007/s00500-006-0106-2
  
• [0572] R. Alcalá, J. Alcalá-Fdez, F. Herrera, J. Otero. Genetic Learning of Accurate and CompactFuzzy Rule Based Systems Based on the 2-Tuples LinguisticRepresentation. International Journal of Approximate Reasoning 44:1 (2007) 45-64. doi: 10.1016/j.ijar.2006.02.007
  
• [0589] M.J. del Jesus, P. González, F. Herrera, M. Mesonero. Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing. IEEE Transactions on Fuzzy Systems 15:4 (2007) 578-592. doi: 10.1109/TFUZZ.2006.890662
  
• [0590] C. García-Martínez, O. Cordón, F. Herrera. A Taxonomy and an Empirical Análisis of Multiple Objective Ant Colony Optimization Algorithms for Bi-criteria TSP. European Journal of Operational Research 180:1 (2007) 116-148. doi: 10.1016/j.ejor.2006.03.041
  
• [0597] F. Chiclana, E. Herrera-Viedma, F. Herrera, S. Alonso. Some Induced Ordered Weighted Averaging Operators and Their Use for Solving Group Decision-Making Problems based on Fuzzy Preference Relations. European Journal of Operational Research, 182:1 (2007) 383-399. doi: 10.1016/j.ejor.2006.08.032
  
• [0598] R. Alcalá, J. Alcalá-Fdez, F. Herrera. A Proposal for the Genetic Lateral Tuning of Linguistic Fuzzy Systems and its Interaction with Rule Selection. IEEE Transactions on Fuzzy Systems 15:4 (2007) 616-635. doi: 10.1109/TFUZZ.2006.889880
  
• [0654] F.A. Márquez, A. Peregrín and F. Herrera. Cooperative Evolutionary Learning of Fuzzy Rules and Parametric Aggregation Connectors for Mamdani Linguistic Fuzzy Systems. IEEE Transactions on Fuzzy Systems, 15:6 (2007) 1162-1178. doi: 10.1109/TFUZZ.2007.904121
  

2006 (3)

• [0342] F. Herrera, M. Lozano, D. Molina. Continuous Scatter Search: An Analysis of the Integration of Some Combination Methods and Improvement Strategies. European Journal of Operational Research 169:2 (2006) 450-476. doi: 10.1016/j.ejor.2004.08.009
  
• [0343] R. Alcalá, J. Alcalá-Fdez, J. Casillas, O. Cordón, F. Herrera. Hybrid learning models to get the interpretability-accuracy trade-off in Fuzzy Modelling. Soft Computing 10:9 (2006) 717-734. doi: 10.1007/s00500-005-0002-1
  
• [0434] J.R. Cano, F. Herrera, M. Lozano. On the Combination of Evolutionary Algorithms and Stratified Strategies for Training Set Selection in Data Mining. Applied Soft Computing 6 (2006) 323-332. doi: 10.1016/j.asoc.2005.02.006
  

2005 (8)

• [0335] F. Herrera, M. Lozano, A.M. Sánchez. Hybrid Crossover Operators for Real-Coded Genetic Algorithms: An Experimental Study. Soft Computing 9:4 (2005) 280-298. doi: 10.1007/s00500-004-0380-9
  
• [0338] F. Herrera, L. Martínez, P.J. Sánchez. Managing Non-Homogeneous Information in Group Decision Making. European Journal of Operational Research 166 (2005) 115-132. doi: 10.1016/j.ejor.2003.11.031
  
• [0339] J. Casillas, O. Cordón, M.J. del Jesús, F. Herrera. Genetic Tuning of Fuzzy Rule Deep Structures Preserving Interpretability and Its Interaction With Fuzzy Rule Set Reduction. IEEE Trans. on Fuzzy Systems 13:1 (2005) 13-29. doi: 10.1109/TFUZZ.2004.839670
  
• [0340] R. Alcalá, J. Casillas, O. Cordón, A. González, F. Herrera. A Genetic Rule Weighting and Selection Process for Fuzzy Control of Heating, Ventilating and Air Conditioning Systems. Engineering Applications of Artificial Intelligence 18:3 (2005) 279-296. doi: 10.1016/j.engappai.2004.09.007
  
• [0347] J. Casillas, O. Cordón, I. Fernández de Viana, F. Herrera. Learning Cooperative Linguistic Fuzzy Rules Using the Best-Worst Ant Systems Algorithm. International Journal of Intelligent Systems 20 (2005) 433-452. doi: 10.1002/int.20074
  
• [0401] J.R. Cano, F. Herrera, M. Lozano. Stratification for Scaling Up Evolutionary Prototype Selection. Pattern Recognition Letters, 26, (2005), 953-963. doi: 10.1016/j.patrec.2004.09.043
  
• [0470] L. Martínez, J. Liu, J.B. Yang, F. Herrera. A Multigranular Hierarchical Linguistic Model for Design Evaluation Based on Safety and Cost Analysis. International Journal of Intelligent Systems 20 (2005) 1161-1194. doi: 10.1002/int.20107
  
• [0504] F. Herrera. Genetic Fuzzy Systems: Status, Critical Considerations and Future Directions. International Journal of Computational Intelligence Research (IJCIR) 1:1 (2005) 59-67.
  

2004 (7)

• [0005] E. Herrera-Viedma, F. Herrera, F. Chiclana, M. Luque. Some Issues on Consistency of Fuzzy Preference Relations. European Journal of Operational Research 154 (2004) 98-109. doi: 10.1016/S0377-2217(02)00725-7
  
• [0331] O. Cordón, F. Gomide, F. Herrera, F. Hoffmann, L. Magdalena. Ten Years of Genetic Fuzzy Systems: Current Framework and New Trends. Fuzzy Sets and Systems 141:1 (2004) 5-31. doi: 10.1016/S0165-0114(03)00111-8
  
• [0332] O. Cordón, F. Herrera, F.A. Márquez, A. Peregrin. A Study on the Evolutionary Adaptive Defuzzification Methods in Fuzzy Modelling. International Journal of Hybrid Intelligent Systems 1:1 (2004) 36-48.
  
• [0333] F. Chiclana, E. Herrera-Viedma, F. Herrera, S. Alonso. Induced Ordered Weighted Geometric Operators and Their Use in the Aggregation of Multiplicative Preference Relations. International Journal of Intelligent Systems 19:3 (2004) 233-255. doi: 10.1002/int.10172
  
• [0334] F. Chiclana, F. Herrera, E. Herrera-Viedma. A Study on the Rationality of Induced Ordered Weighted Operators Based on the Reliability of the Information Sources for Aggregation for Group Decision-Making. Kybernetika 40:1 (2004) 121-142.
  
• [0336] E. Herrera-Viedma, F. Herrera, L. Martínez, J.C. Herrera, A.G. López-Herrera. Incorporating Filtering Techniques in a Fuzzy Linguistic Multi-Agent Model for Information Gathering on the Web. Fuzzy Sets and Systems 148:1 (2004) 61-83. doi: 10.1016/j.fss.2004.03.006
  
• [0337] M. Lozano, F. Herrera, N. Krasnogor, D. Molina. Real-Coded Memetic Algorithms with Crossover Hill-Climbing. Evolutionary Computation 12:3 (2004) 273-302. doi: 10.1162/1063656041774983
  

2003 (9)

• [0004] F. Herrera, E. Herrera-Viedma, F. Chiclana. A Study of the Origin and Uses of the Ordered Weighted Geometric Operator in Multicriteria Decision Making. International Journal of Intelligent Systems 18:6 (2003) 689-707. doi: 10.1002/int.10106
  
• [0010] F. Herrera, M. Lozano. Fuzzy Adaptive Genetic Algorithms: Design, Taxonomy and Future Directions. Soft Computing 7:8 (2003) 545-562. doi: 10.1007/s00500-002-0238-y
  
• [0011] R. Alcalá, J.R. Cano, O. Cordón, F. Herrera, P. Villar, I. Zwir. Linguistic Modeling with Hierarchical Systems of Weighted Linguistic Rules. International Journal of Approximate Reasoning 32:2-3 (2003) 187-215. doi: 10.1016/S0888-613X(02)00083-X
  
• [0013] R. Alcalá, J. Casillas, O. Cordón, F. Herrera. Linguistic Modeling with Weighted Double-Consequent Fuzzy Rules Based on Cooperative Coevolutionary Learning. Integrated Computer Aided Engineering 10 (4) (2003) 343-355.
  
• [0014] F. Chiclana, F. Herrera, E. Herrera-Viedma, L. Martínez. A Note on the Reciprocity in the Aggregation of Fuzzy Preference Relations Using OWA Operators. Fuzzy Sets and Systems 137:1 (2003) 71-83. doi: 10.1016/S0165-0114(02)00433-5
  
• [0015] O. Cordón, F. Herrera, I. Zwir. A Hierarchical Knowledge-Based Environment for Linguistic Modeling: Models and Iterative Methodology. Fuzzy Sets and Systems 138:2 (2003) 307-341.
  
• [0018] E. Pérez, F. Herrera, C. Hernández. Finding Multiple Solutions in Job Shop Scheduling by Niching Genetic Algorithm. Journal of Intelligent Manufacturing 14:3-4 (2003) 323-339. doi: 10.1023/A:1024649709582
  
• [0298] F. Herrera, M. Lozano, A.M. Sánchez. A Taxonomy for the Crossover Operator for Real-Coded Genetic Algorithms: An Experimental Study. International Journal of Intelligent Systems 18 (2003) 309-338. doi: 10.1002/int.10091
  
• [0361] J.R. Cano, F. Herrera, M. Lozano. Using Evolutionary Algorithms as Instance Selection for Data Reduction in KDD: An Experimental Study. IEEE Trans. on Evolutionary Computation 7:6 (2003) 561-575. doi: 10.1109/TEVC.2003.819265
  

2002 (9)

• [0012] O. Cordón, I. Fernández de Viana, F. Herrera. Analysis of the Best-Worst Ant System and its Variants on the TSP. Mathware and Soft Computing 9:2-3 (2002) 177-192.
  
• [0016] E. Herrera-Viedma, F. Herrera, F. Chiclana. A Consensus Model for Multiperson Decision Making with Different Preference Structures. IEEE Transactions on Systems, Man and Cybernetics. Part A: Systems and Man 32:3 (2002) 394-402. doi: 10.1109/TSMCA.2002.802821
  
• [0023] J. Casillas, O. Cordón, F. Herrera. COR: A Methodology to Improve Ad Hoc Data-Driven Linguistic Rule Learning Methods by Inducing Cooperation Among Rules. IEEE Transactions on Systems, Man, and Cybernetics. Part B: Cybernetics 32:4 (2002) 526-537. doi: 10.1109/TSMCB.2002.1018771
  
• [0024] M. Delgado, F. Herrera, E. Herrera-Viedma, M.J. Martin-Bautista, L. Martinez, M.A. Vila. A Communication Model Based on the 2-tuple Fuzzy Linguistic Representation for a Distributed Intelligent Agent System on Internet. Soft Computing 6:5 (2002) 320-328. doi: 10.1007/s00500-002-0185-7
  
• [0025] O. Cordón, F. Herrera, I. Zwir. Linguistic Modeling by Hierarchical Systems of Linguistic Rules. IEEE Transactions on Fuzzy Systems 10:1 (2002) 2-20. doi: 10.1109/91.983275
  
• [0026] F. Herrera, E. López, M.A. Rodríguez. A Linguistic Decision Model for Promotion Mix Management Solved with Genetic Algorithms. Fuzzy Sets and Systems 131:1 (2002) 47-61. doi: 10.1016/S0165-0114(01)00254-8
  
• [0027] O. Cordón, F. Herrera, T. Stützle. A Review of the Ant Colony Optimization Metaheuristic: Basis, Models and New Trends. Mathware and Soft Computing 9:2-3 (2002) 141-175.
  
• [0031] F. Chiclana, F. Herrera, E. Herrera-Viedma. A Note on the Internal Consistency of Various Preference Representations. Fuzzy Sets and Systems 131:1 (2002) 75-78. doi: 10.1016/S0165-0114(01)00256-1
  
• [0178] J.R. Cano, O. Cordón, F. Herrera, L. Sánchez. A Greedy Randomized Adaptive Search Procedure Applied to the Clustering Problem as an Initialization Process Using K-Means as a Local Search Procedure. International Journal of Intelligent and Fuzzy Systems 12 (2002) 235-242.
  

2001 (14)

• [0017] R. Alcalá, J. Casillas, O. Cordón, F. Herrera. Building Fuzzy Graphs: Features and Taxonomy of Learning Non-Grid-Oriented Fuzzy Rule-Based Systems. International Journal of Intelligent Fuzzy Systems, 11 (2001) 99-119.
  
• [0019] R. Alcalá, J. Casillas, J.L. Castro, A. Gonzalez, F. Herrera. A Multicriteria Genetic Tuning for Fuzzy Logic Controllers. Mathware and Soft Computing, Vol. 8:2 (2001) 179-201.
  
• [0020] F. Herrera, L. Martínez. The 2-tuple Linguistic Computational Model. Advantages of its linguistic description, accuracy and consistency. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 9:suppl. (Sept. 2001) 33-48.
  
• [0021] O. Cordón, F. Herrera, P. Villar. Generating the Knowledge Base of a Fuzzy Rule-Based System by the Genetic Learning of Data Base. IEEE Transactions on Fuzzy Systems 9:4 (2001) 667-674.
  
• [0032] J. Casillas, O. Cordón, F. Herrera, M.J. Del Jesus. Genetic Feature Selection in a Fuzzy Rule-Based Classification System Learning Process for High-Dimensional Problems. Information Sciences 136:1-4 (2001) 135-157.
  
• [0033] F. Herrera, M. Lozano. Adaptative Genetic Operators Based on Coevolution with Fuzzy Behaviors. IEEE Transactions on Evolutionary Computation 5:2 (2001) 149-165.
  
• [0035] R. Alcalá, J. Casillas, O. Cordón, F. Herrera. Improvement to the Cooperative Rules Methodology by Using the Ant Colony System Algorithm. Mathware and Soft Computing Vol 8:3 (2001) 321-335.
  
• [0036] O. Cordón, F. Herrera, L. Magdalena, P. Villar. A Genetic Learning Process for the Scaling Factors, Granularity and Contexts of the Fuzzy Rule-Based System Data Base. Information Science 136 (2001) 85-107.
  
• [0039] O. Cordón, F. Herrera, I. Zwir. Fuzzy Modeling by Hierarchically Built Fuzzy Rule Bases. International Journal of Approximate Reasoning 27 (2001) 61-93.
  
• [0040] F. Chiclana, F. Herrera, E. Herrera-Viedma. Integrating Multiplicative Preference Relations in a Multipurpose Decision Making Model Based on Fuzzy Preference Relations. Fuzzy Sets and Systems 122 (2001), 277-291.
  
• [0041] F. Herrera, L. Martínez. A model based on linguistic 2-tuples for dealing with multigranularity hierarchical linguistic contexts in Multiexpert Decision-Making. IEEE Transactions on Systems, Man, and Cybernetics. Part B: Cybernetics 31:2 (2001) 227-234.
  
• [0042] F. Chiclana, F. Herrera, E. Herrera-Viedma. Multiperson Decision Making Based on Multiplicative Preference Relations. European Journal of Operational Research 129 (2001) 372-385.
  
• [0043] O. Cordón, F. Herrera. Hybridizing Genetic Algorithms with Sharing Scheme and Evolution Strategies for Designing Approximate Fuzzy Rule-Based Systems. Fuzzy Sets and Systems 118:2 (2001) 235-255.
  
• [0044] F. Herrera, E. López, C. Mendaña, M.A. Rodríguez. A Linguistic Decision Model for Personnel Management Solved with a Linguistic Biobjetictive Genetic Algorithm. Fuzzy Sets and Systems 118:1 (2001) 47-64.
  

2000 (10)

• [0046] F. Herrera, L. Martínez. A 2-tuple Fuzzy Linguistic Representation Model for Computing with Words. IEEE Transactions on Fuzzy Systems 8:6 (2000) 746-752.
  
• [0047] F. Herrera, L. Martínez. An Approach for Combining Numerical and Linguistic Information based on the 2-tuple fuzzy linguistic representation model in Decision Making. International Journal of Uncertainty , Fuzziness and Knowledge -Based Systems 8:5 (2000) 539-562.
  
• [0048] F. Herrera, M. Lozano. Two-Loop Real-Coded Genetic Algorithms with Adaptive Control of Mutation Step Sizes. Applied Intelligence 13:3 (2000) 187-204.
  
• [0049] O. Cordón, F. Herrera, P. Villar. Analysis and Guidelines to Obtain a Good Fuzzy Partition Granularity for Fuzzy Rule-Based Systems using Simulated Annealing. International Journal of Approximate Reasoning 25:3 (2000) 187-215.
  
• [0050] O. Cordón, F. Herrera. A Proposal for Improving the Accuracy of Linguistic Modeling. IEEE Transactions on Fuzzy Systems 8:3 (2000) 335-344.
  
• [0051] F. Herrera, M. Lozano. Gradual Distributed Real-Coded Genetic Algorithms. IEEE Transactions on Evolutionary Computation 4:1 (2000) 43-63.
  
• [0052] F. Herrera, E. Herrera-Viedma. Choice Functions and Mechanisms for Linguistic Preference Relations. European Journal of Operational Research 120 (2000) 144-161.
  
• [0053] F. Herrera, E. Herrera-Viedma, L. Martinez. A Fusion Approach for Managing Multi-Granularity Linguistic Term Sets in Decision Making. Fuzzy Sets and Systems 114 (2000) 43-58.
  
• [0054] O. Cordón, F. Herrera, A. Peregrín. Searching for Basic Properties Obtaining Robust Implication Operators in Fuzzy Control. Fuzzy Sets and Systems 111:2 (2000) 237-251.
  
• [0055] F. Herrera, E. Herrera-Viedma. Linguistic Decision Analysis: Steps for Solving Decision Problems under Linguistic Information. Fuzzy Sets and Systems 115 (2000) 67-82.
  

1999 (7)

• [0056] O. Cordón, F. Herrera. A Two-Stage Evolutionary Process for Designing TSK Fuzzy Rule-Based Systems. IEEE Transactions on Systems, Man,and Cybernetics. Part B: Cybernetics Vol. 29:6 (December 1999) 703-715.
  
• [0057] O. Cordón, F. Herrera, A. Peregrín. A Practical Study on the Implementation of Fuzzy Logic Controllers. The International Journal of Intelligent Control and Systems 3 (1999) 49-91.
  
• [0058] F. Herrera, M. Lozano, C. Moraga. Hierarchical Distributed Genetic Algorithms. International Journal of Intelligent Systems 14:11 (1999) 1099-1121.
  
• [0059] O. Cordón, M. J. del Jesus, F. Herrera, M. Lozano. MOGUL: A Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach. International Journal of Intelligent Systems Vol. 14:11 (1999) 1123-1153.
  
• [0060] F. Herrera, E. López, C. Mendaña, M.A. Rodríguez. Solving an Assignment-Selection Problem Under Linguistic Valuations with Genetic Algorithms. European Journal of Operational Research 119:2 (1999) 326-337.
  
• [0061] O. Cordón, M.J. del Jesus, F. Herrera. A Proposal on Reasoning Methods in Fuzzy Rule-Based Classification Systems. International Journal of Approximate Reasoning Vol. 20 (1999), 21-45.
  
• [0062] O. Cordón, F. Herrera, L. Sánchez. Solving Electrical Distribution Problems Using Hybrid Evolutionary Data Analysis Techniques. Applied Intelligence 10 (1999) 5-24.
  

1998 (8)

• [0063] O. Cordón, M.J. del Jesus, F. Herrera. Analyzing the Reasoning Mechanisms in Fuzzy Rule-Based Classification Systems. Mathware and Soft Computing. Vol. 5: 2-3 (1998), 321-332.
  
• [0064] O. Cordón, M.J. del Jesus, F. Herrera. Genetic Learning of Fuzzy Rule-Based Classification Systems Cooperating with Fuzzy Reasoning Methods. International Journal of Intelligent Systems 13:10-11 (1998) 1025-1053.
  
• [0065] F. Herrera, M. Lozano, J.L. Verdegay. A Learning Process for Fuzzy Control Rules using Genetic Algorithms. Fuzzy Sets and Systems 100 (1998) 143-158.
  
• [0066] M. Delgado, F. Herrera, E. Herrera-Viedma, L. Martinez. Combining Numerical and Linguistic Information in Group Decision Making. Information Sciences 107 (1998) 177-194.
  
• [0067] F. Herrera, M. Lozano, J.L. Verdegay. Tackling Real-Coded Genetic Algorithms: Operators and tools for the Behaviour Analysis. Artificial Intelligence Review 12 (1998) 265-319.
  
• [0068] F. Chiclana, F. Herrera, E. Herrera-Viedma. Integrating Three Representation Models in Fuzzy Multipurpose Decision Making Based on Fuzzy Preference Relations. Fuzzy Sets and Systems 97 (1998) 33-48.
  
• [0069] O. Cordón, M.J. del Jesus, F. Herrera, M. Lozano. Modelado Cualitativo Utilizando una Metodología Evolutiva de Aprendizaje Iterativo de Bases de Reglas Difusas. Revista Iberoamericana de la Asociación Española para la Inteligencia Artificial. Num. 5 (1998), 56-61.
  
• [0070] F. Herrera, E. Herrera-Viedma, J.L. Verdegay. Choice Processes for Non-Homogeneous Group Decision Making in Linguistic Setting. Fuzzy Sets and Systems. Vol. 94 (1998) 287-308.
  

1997 (9)

• [0071] F. Herrera, E. Herrera-Viedma. Aggregation Operators for Linguistic Weighted Information. IEEE Transactions on Systems, Man and Cybernetics. 27 (1997) 646-656.
  
• [0072] O. Cordón, F. Herrera. A Three-Stage Evolutionary Process for Learning Descriptive and Approximative Fuzzy Logic Controller Knowledge Bases from Examples. International Journal of Approximate Reasoning 17:4 (1997) 369-407.
  
• [0073] A. Gonzalez, F. Herrera. Multi-Stage Genetic Fuzzy Systems Based on the Iterative Rule Learning Approach. Mathware and Soft Computing 4:3 (1997) 233-249.
  
• [0074] F. Herrera, J.L. Verdegay. Fuzzy Sets and Operations Research. Perspectives. Fuzzy Sets and Systems 90:2 (1997) 207-218.
  
• [0075] F. Herrera, M. Lozano, J.L. Verdegay. Fuzzy Connectives Based Crossover Operators to Model Genetic Algorithms Population Diversity. Fuzzy Sets and Systems. Vol. 92-1 (1997) 21-30.
  
• [0076] F. Herrera, L. Magdalena. Genetic Fuzzy Systems. Tatra Mountains Mathematical Publications Vol. 13, 1997, 93-121. R. Mesiar,B. Riecan(Eds.) Fuzzy Structures. Current Trends. L. N. Tutorial: Genetic Fuzzy Systems.7th IFSA World Congress,Prage,June 97.
  
• [0077] F. Herrera, E. Herrera-Viedma, J.L. Verdegay. Linguistic Measures Based on Fuzzy Coincidence for Reaching Consensus in Group Decision Making. International Journal of Approximate Reasoning 16 (1997), 309-334.
  
• [0078] F. Herrera, E. Herrera-Viedma, J.L. Verdegay. A Rational Consensus Model in Group Decision Making using Linguistic Assessments. Fuzzy Sets and Systems Vol. 88:1 (1997), 31-49.
  
• [0079] O. Cordón, F. Herrera, A. Peregrín. Applicability of the Fuzzy Operators in the Design of Fuzzy Logic Controllers. Fuzzy Sets and Systems Vol. 86:1 (1997), 15-41.
  

1996 (8)

• [0080] F. Herrera, M. Lozano, J.L. Verdegay. Dynamic and Heuristic Fuzzy Connectives-Based Crossover Operators for Controlling the Diversity and Convengence of Real Coded Genetic Algorithms. Int. Journal of Intelligent Systems Vol. 11 (1996) 1013-1041.
  
• [0081] F. Herrera, J.L. Verdegay. Fuzzy Control Rules in Optimization. International Journal of Science & Technology: Scientia Iranica. Vol. 3. no. 1,2,3 (1996) 89-96.
  
• [0082] O. Cordón, F. Herrera, E. Herrera-Viedma, M. Lozano. Genetic Algorithms and Fuzzy Logic in Control Processes. Archives of Control Sciences. Vol. 5 (1996) 135-168.
  
• [0083] F. Chiclana, F. Herrera, E. Herrera-Viedma, M.C. Poyatos. A classification method of alternatives for multiple preference ordering criteria based on fuzzy majority. The Journal of Fuzzy Mathematics 4 (1996) 801-813.
  
• [0084] F. Herrera, J.L. Verdegay. Fuzzy Boolean Programming Problems with Fuzzy Cost: A general study. Fuzzy Sets and Systems Vol. 81 (1996) 57-76.
  
• [0085] F. Herrera, E. Herrera-Viedma, J.L. Verdegay. Direct Approach Processes in Group Decision Making using Linguistic OWA operators. Fuzzy Sets and Systems 79 (1996) 175-190.
  
• [0086] F. Herrera, E. Herrera-Viedma, J.L. Verdegay. A model of consensus in group decision making under linguistic assessments. Fuzzy Sets and Systems 78 (1996) 73-87.
  
• [0087] F. Herrera, E. Herrera-Viedma, J.L. Verdegay. A Linguistic Decision Process in Group Decision Making. Group Decision and Negotiation. 5 (1996) 165-176.
  

1995 (8)

• [0088] F. Herrera, M. Lozano, J.L. Verdegay. The Use of Fuzzy Connectives to Design real-Coded Genetic Algorithms. Mathware and Soft Computing. Vo. 1:3 (1995) 239-251.
  
• [0089] F. Herrera, E. Herrera-Viedma, J.L. Verdegay. Preference Degrees over Linguistic Preference Relations in Decision Making. Operational Research and Decisions 3 (1995) 37-48.
  
• [0090] F. Herrera, E. Herrera-Viedma, J.L. Verdegay. A Sequential Selection Process in Group Decision Making with a Linguistic Assessment Approach. Information Science 85 (1995) 223-239.
  
• [0091] F. Herrera, J.L. Verdegay. Three Models of Fuzzy Integer Linear Programming. European Journal of Operational Research 83 (1995) 581-593.
  
• [0092] F. Herrera, M. Lozano, J.L. Verdegay. Tuning Fuzzy Logic Controllers by Genetic Algorithms. International Journal of Approximate Reasoning 12 (1995) 299-315. doi: 10.1016/0888-613X(94)00033-Y
  
• [0093] F. Herrera, M. Lozano, J.L. Verdegay. Algoritmos Geneticos: Fundamentos, Extensiones y Aplicaciones. ARBOR CLII 597 (1995) 9-40.
  
• [0094] F. Herrera, M. Lozano, J.L. Verdegay. Algoritmos Geneticos con Parametros Reales. NOVATICA, May/jun 115 (1995) 36-41.
  
• [0095] E. Cárdenas, J.C. Castillo, O. Cordón, F. Herrera, A. Peregrín. Applicability of T-Norms in Fuzzy Control. BUSEFAL 61 (1995) 28-36.
  

1994 (5)

• [0096] F. Herrera, M. Lozano, J.L. Verdegay. Applying Genetic Algorithms in Fuzzy Optimization Problems. Fuzzy Systems and A.I.-Reports and Letters 3 (1994) 39-52.
  
• [0097] F. Herrera, J.L. Verdegay. Fuzzy Almost Integer Variables in Integer Programming Problems. The Journal of Fuzzy Mathematics 2:2 (1994) 259-270.
  
• [0098] F. Herrera, M. Kovacs, J.L. Verdegay. Homogeneous Linear Fuzzy Functions and Ranking Methods in Fuzzy Linear Programming Problems. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 2:1 (1994) 25-35.
  
• [0099] E. Cárdenas, J.C. Castillo, O. Cordón, F. Herrera, A. Peregrín. Influence of Fuzzy Implication Functions and Defuzzification Methods in Fuzzy Control. BUSEFAL 57 (1994) 69-79.
  
• [0100] J.L. Castro, F. Herrera, J.L. Verdegay. Knowledge based systems and Fuzzy Boolean Programming. International Journal on Intelligent Systems 9:2 (1994) 211-225. doi: 10.1002/int.4550090203
  

1993 (4)

• [0101] F. Herrera, M. Kovacs, J.L. Verdegay. A Parametric Approach for (G,P)-Fuzzified Linnear Programming Problems. The Journal of Fuzzy Mathematics 1:3 (1993) 699-713.
  
• [0102] F. Herrera, J.L. Verdegay, H.-J. Zimmermann. Boolean Programming Problems with Fuzzy Constraints. Fuzzy Systems and Systems 55:3 (1993) 285-293.
  
• [0103] F. Herrera, M. Kovacs, J.L. Verdegay. Optimality for Fuzzified Mathematical Programming Problems: A Parametric Approach. Fuzzy Systems and Systems 54:3 (1993) 279-285.
  
• [0104] M. Delgado, F. Herrera, J.L. Verdegay, A. Vila. Post-optimality Analysis on the Membership Functions of a Fuzzy Linear Programming Problem. Fuzzy Systems and Systems 53:3 (1993) 289-297.
  

1992 (2)

• [0105] F. Herrera, M. Kovacs, J.L. Verdegay. An Optimum Concept for Fuzzified Linear Programming Problems: A Parametric Approach. Tatra Mountains Mathematical Publications 1 (1992) 57-64.
  
• [0106] M. Delgado, F. Herrera, J.L. Verdegay, M.A. Vila. Fuzzy Linear Programming Problems with Nonlinear Membership. Fuzzy Systems and A.I. Reports and Letters 1:1 (1992) 33-49.