SCI2S Publications (A. Fernandez)

Number of Results: 147

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2024 (2)

• [3094] S. Giannoukakos, S. D'Ambrosi, D. Koppers-Lalic, C. Gómez-Martín, A. Fernandez, M. Hackenberg. Assessing the complementary information from an increased number of biologically relevant features in liquid-biopsy-derived RNA-Seq datarr. Heliyon 10:6e27360 (2024). doi: 10.1016/j.heliyon.2024.e27360
  
• [3101] J. Lopez-Martinez, P. Checa, J.M. Soto-Hidalgo, I. Triguero, A. Fernández. A Wearable Eye-Tracking Approach for Early Autism Detection with Machine Learning: Unravelling Challenges and Opportunities. 2024 International Joint Conference on Neural Networks (IJCNN).
  

2023 (4)

• [2935] J. Pereira Amorim, P.H. Abreu, A. Fernández, M. Reyes, J. Santos, M. H. Abreu. Interpreting Deep Machine Learning Models: An Easy Guide for Oncologists. IEEE Reviews in Biomedical Engineering, 16 (2023) 192 - 207. doi: 10.1109/RBME.2021.3131358
  
• [2997] M.S. Santos, P.H. Abreu, N. Japkowicz, A. Fernández, J. Santos. A unifying view of class overlap and imbalance: Key concepts, multi-view panorama, and open avenues for research. Information Fusion 89 (2023) 228-253. doi: 10.1016/j.inffus.2022.08.017
  
• [3032] F. Aghaei, M. Sabokrou, A. Fernandez. Fuzzy Rule-Based Explainer Systems for Deep Neural Networks: From Local Explainability to Global Understanding. IEEE Transactions on Fuzzy Systems 31:9 (2023) 3069-3080. doi: 10.1109/TFUZZ.2023.3243935
  
• [3041] S. D'Ambrosi, S. Giannoukakos, M. Antunes-Ferreira, C. Pedraz-Valdunciel, J. W. P. Bracht, N. Potie, A. Gimenez-Capitan, M. Hackenberg, A. Fernandez, M. A. Molina-Vila, R. Rosell, T. Würdinger, D. Koppers-Lalic. Combinatorial Blood Platelets-Derived circRNA and mRNA Signature for Early-Stage Lung Cancer Detection. International Journal of Molecular Sciences 24:5 (2023) 4881:1-4881:17. doi: 10.3390/ijms24054881
  

2022 (8)

• [2876] F. Aghaeipoor, M. M. Javidi, A. Fernandez. IFC-BD: An Interpretable Fuzzy Classifier for Boosting Explainable Artificial Intelligence in Big Data. IEEE Transactions on Fuzzy Systems 30:3 (2021) 830-840. doi: 10.1109/TFUZZ.2021.3049911
  
• [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
  
• [2942] C- Pedraz-Valdunciel, S. Giannoukakos, N. Potie, A. Gimenez-Capitan,C-Y Huang,M. Hackenberg, A. Fernandez, J. Bracht, M. Filipska, E. Aldeguer, S. Rodriguez, T. G. Bivona, S. Warren, C. Aguado, M. Ito, A. Aguilar-Hernandez, M.A. Molina-Vila. R. Rosell. Digital multiplexed analysis of circular RNAs in FFPE and fresh non-small cell lung cancer specimens. Molecular Oncology 16:12 (2022) 2367-2383. doi: 10.1002/1878-0261.13182
  
• [2943] M.S. Santos, P.H. Abreu, A. Fernández, J. Luengo, J. Santos. The impact of heterogeneous distance functions on missing data imputation and classification performance. Engineering Applications of Artificial Intelligence, 111 (2022) 104791. doi: 10.1016/j.engappai.2022.104791
  
• [2959] M.S. Santos, P.H. Abreu, N. Japkowicz, A. Fernández, C. Soares, S. Wilk, J. Santos. On the joint-efect of class imbalance and overlap: a critical review. Artifcial Intelligence Review 55 (2022) 6207–6275. doi: 10.1007/s10462-022-10150-3
  
• [2977] X. Chao, G. Kou, Y. Peng. A. Fernández. An Efficiency Curve for Evaluating Imbalanced Classifiers Considering Intrinsic Data Characteristics: Experimental Analysis. Information Sciences 608 (2022) 1131-1156. doi: 10.1016/j.ins.2022.06.045
  
• [3045] Pedraz-Valdunciel, C; Giannoukakos, S; Gimenez-Capitan, A; Fortunato, D; Filipska, M; Bertran-Alamillo, J; Bracht, JWP; Drozdowskyj, A; Valarezo, J; Zarovni, N; A. Fernandez; Hackenberg, M; Aguilar-Hernandez, A; Molina-Vila, MA; Rosell, R.. Multiplex Analysis of CircRNAs from Plasma Extracellular Vesicle-Enriched Samples for the Detection of Early-Stage Non-Small Cell Lung Cancer. Pharmaceutics 14:10 (2022) 2034:1-2034:10. doi: 10.3390/pharmaceutics14102034
  
• [3046] F. Aghaeipoor, A. Fernandez.. IFC-BD: An Interpretable Fuzzy Classifier for Boosting Explainable Artificial Intelligence in Big Data (keywork). XXI Congreso Español sobre Tecnologías y Lógica Fuzzy. ESTYLF 2022, Toledo (Spain), 101-102, Sept. 5-7, 2022.
  

2021 (4)

• [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
  
• [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
  
• [2922] M.J. Basgall, M. Naiouf, A. Fernandez. FDR2-BD: A Fast Data Reduction Recommendation Tool for Tabular Big Data Classification Problems. Electronics 10:15 (2021) 1757. doi: 10.3390/electronics10151757
  
• [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
  

2020 (4)

• [2744] N. Potie, S. Giannoukakos, M. Hackenberg, A. Fernandez. Applying Feature Selection to Improve Predictive Performance and Explainability in Lung Cancer Detection with Soft Computing. Proceedings of the 53rd Hawaii International Conference on System Sciences, (HCISS 2020), Maui (USA), 1727-1736, 2020.
  
• [2863] F. Aghaeipoor, M. M. Javidi, I. Triguero, A. Fernández. Chi-BD-DRF: Design of Scalable Fuzzy Classifiers for Big Data via A Dynamic Rule Filtering Approach. 2020 IEEE World Congress on Computational Intelligence (WCCI) - 2020 FUZZ-IEEE, Glasgow (UK), 1-7, July 19-24, 2020.
  
• [2864] J.R. Trillo, A. Fernández, F. Herrera. HFER: Promoting Explainability in Fuzzy Systems via Hierarchical Fuzzy Exception Rules. 2020 IEEE World Congress on Computational Intelligence (WCCI) - 2020 FUZZ-IEEE, Glasgow (UK), 1-8, July 19-24, 2020.
  
• [2865] M.J. Basgall, M. Naiouf, A. Fernandez, F. Herrera. Towards Smart Data Technologies for Big Data Analytics. Short Papers of the 8th Conference on Cloud Computing Conference, Big Data & Emerging Topics (JCC-BD&ET 2020), La Plata (Buenos Aires), September 8-10 2020, 44-47.
  

2019 (7)

• [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
  
• [2706] M.J. Basgall, W. Hasperué, M. Naiouf, A. Fernandez, F. Herrera. An Analysis of Local and Global Solutions to Address Big Data Imbalanced Classification: A Case Study with SMOTE Preprocessing. M. Naiouf et al. (Eds.): JCC&BD 2019, CCIS 1050, pp. 75–85, 2019. doi: 10.1007/978-3-030-27713-0_7
  
• [2707] N. Potie, S. Giannoukakos, M. Hackenberg, A. Fernandez. On the Need of Interpretability for Biomedical Applications: Using Fuzzy Models for Lung Cancer Prediction with Liquid Biopsy. 2019 Internacional Conference on Fuzzy Systems (FUZZ-IEEE 2019), New Orleans (USA), 1-6, June 23-26, 2019.
  
• [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
  
• [2712] P. García-Sánchez, A. Tonda, A. Fernández-Leiva, C. Cotta, M.J. Cobo. Optimización de agentes para HearthStone utilizando Algoritmos Evolutivos. Actas de las IV Jornadas Andaluzas de Informática (JAI 2019), Canillas de Aceituno, Málaga (España), pp. 27-30, 27-29Septiembre (2019). ISBN 978-84-17171-54-4.
  

2018 (12)

• [2596] A. Fernández, S. García, M. Galar, R.C. Prati, B. Krawczyk, F. Herrera. Learning from Imbalanced Data Sets. Springer International Publishing, 2018, ISBN 978-3-319-98073-7. doi: 10.1007/978-3-319-98074-4
  
• [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
  
• [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
  
• [2470] S. Elhag, A. Fernandez, S. Alshomrani, F. Herrera. Evolutionary Fuzzy Systems: A Case Study for Intrusion Detection Systems. In: J.C. Bansal, P.K. Singh, N.R. Pal (Eds). Evolutionary and Swarm Intelligence Algorithms; Part of the Studies in Computational Intelligence book series (SCI, volume 779), Pages 169-190. doi: 10.1007/978-3-319-91341-4_9
  
• [2474] L. Iñiguez, M. Galar, A. Fernandez. Improving Fuzzy Rule Based Classification Systems in Big Data via Support-based Filtering. 2018 World Congress on Computational Intelligence (WCCI-2018), 2018 FUZZ-IEEE Conference (FUZZ-IEEE 2018), Rio de Janeiro (Brasil), 531-538.
  
• [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
  
• [2487] 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. Proceedings of MOL2NET 2018, International Conference on Multidisciplinary Sciences, 4th edition, 1. doi: 10.3390/mol2net-04-05468
  
• [2574] 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. XVIII Conferencia de la Asociacion Española para la Inteligencia Artificial (CAEPIA-2018), Granada (Spain), 1316-1317, 23-26 October 2018.
  
• [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.
  
• [2577] 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. VI Jornadas de Cloud Computing & Big Data (JCC&BD2018), La Plata -Buenos Aires- (Argentina), 23-28, 25-29 Junio 2018.
  

2017 (8)

• [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
  
• [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
  
• [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
  
• [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.
  
• [2336] A. Fernandez, E. Almansa, F. Herrera. Chi-Spark-RS: An Spark-built evolutionary fuzzy rule selection algorithm in imbalanced classification for big data problems. 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples (Italy), 1-6 July 9-12, 2017. doi: 10.1109/FUZZ-IEEE.2017.8015520
  
• [2424] C. del Val, E. Ruiz, R. Alcalá, A. Fernández, C. Cano, W. Fajardo, J. Alcalá-Fdez. Can Bioinformatics close the gender gap in STEM skills? : Reflections from the I Bioinformatics UGR Workshop. III Jornadas Andaluzas de Informática (JAI 2017), Canillas de Aceituno (Spain), 14-17, September 22-24, 2017.
  

2016 (8)

• [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
  
• [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
  
• [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
  
• [2085] A. Fernandez, F. Herrera. Evolutionary Fuzzy Systems: A Case Study in Imbalanced Classification. Fuzzy Logic and Information Fusion, Volume 339 of the series Studies in Fuzziness and Soft Computing (2016) 169-200. doi: 10.1007/978-3-319-30421-2_12
  
• [2092] A. Fernandez, S. Río, F. Herrera. Sistemas de Clasificación Basados en Reglas Difusas para Big Data con MapReduce: Análisis de la Granularidad. XVIII Congreso Español sobre Tecnologías y Lógica Fuzzy. ESTYLF 2016, San Sebastián (Spain), 1-2, May 25-27, 2016.
  
• [2094] A. Fernandez, M. Galar, E. Barrenechea, H. Bustince, F. Herrera. Ordering-Based Pruning for Improving the Performance of Ensembles of Classiers in the Framework of Imbalanced Datasets. XVII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA), Salamanca (Spain), 1-2, September 14-16, 2016.
  
• [2095] A. Fernandez, S. Río, F. Herrera. A First Approach in Evolutionary Fuzzy Systems based on the Lateral Tuning of the Linguistic Labels for Big Data Classification. 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016), Vancouver (Canada), 1-8, July 24-29, 2016.
  
• [2177] M. Espinilla, A. Fernández, J. Santamaría, A. Rivera. Gamificación en procesos de autoentrenamiento y autoevaluación. Experiencia en la asignatura de Arquitectura de Computadores. Revista de Experiencias Docentes en Ingeniería de Computadores 6 (2016) 55-66.
  

2015 (14)

• [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
  
• [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
  
• [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
  
• [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
  
• [1958] P. Villar, A. Fernández, R. Montes, A.M. Sánchez, F. Herrera. Improving the OVO performance in fuzzy rule-based classification systems by the genetic learning of the granularity level. 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015), Estambul (Turkey) august 2-5, 2015.
  
• [1981] 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. V Symposium of Fuzzy Logic and Soft Computing (LODISCO 2015) - CAEPIA 2015, Albacete (España), pp. 1-2, 09-12 Noviembre 2015.
  
• [1982] A. Fernandez, M. Elkano, M. Galar, J.A. Sanz, H. Bustince, F. Herrera. Mejorando los Sistemas Difusos Evolutivos para Problemas Multi-Clase: Ponderando la Competencia en el Modelo Uno-contra-Uno con Truncado de las Confianzas. II Symposium on Information Fusion and ensembles (FINO 2015) - CAEPIA 2015, Albacete (España), pp. 1-10, 09-12 Noviembre 2015.
  
• [1983] M. Galar, A. Fernández, E. Barrenechea, H. Bustince, F. Herrera. Nuevas Metricas sobre Poda basada en Orden para Ensembles de Clasicadores en Conjuntos de Datos No Balanceados. II Symposium on Information Fusion and ensembles (FINO 2015) - CAEPIA 2015, Albacete (España), pp. 1-10, 09-12 Noviembre 2015.
  
• [1984] A. Fernandez, M. Galar, J.A. Sanz, H. Bustince, O. Cordón, F. Herrera. On the Impact of Distance-based Relative Competence Weighting Approach in One-vs-One Classification for Evolutionary Fuzzy Systems: DRCW-FH-GBML algorithm. 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015), Estambul (Turquía) pp. 15124-15131, 02-05 Agosto 2015.
  
• [1999] A. Fernandez, M.J. del Jesus, F. Herrera. Addressing Overlapping in Classification with Imbalanced Datasets: A First Multi-objective Approach for Feature and Instance Selection. 16th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2015), Lecture Notes in Computer Science (LNCS) 9375, Wroclaw (Poland), 36-44, October 14-16, 2015.
  
• [2000] A. Fernandez, M. Galar, J.A. Sanz, H. Bustince, F. Herrera. Improving Pairwise Learning Classification in Fuzzy Rule Based Classification Systems Using Dynamic Classifier Selection. 16th World Congress of the International Fuzzy Systems Association (IFSA), 9th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), Gijón (Spain) 1020-1026, June 30, July 03, 2015.
  
• [2001] P. Villar, A. Fernandez, F. Herrera. On the combination of pairwise and granularity learning for improving fuzzy rule based classication systems: GL-FARCHD-OVO. 9th International Conference on Computer Recognition Systems (CORES 2015), Wroclaw (Poland), 1-8, May 25-27, 2015.
  
• [2002] M. Galar, A. Fernandez, E. Barrenechea, H. Bustince, F. Herrera. New Ordering-Based Pruning Metrics for Ensembles of Classiers in Imbalanced Datasets. 9th International Conference on Computer Recognition Systems (CORES 2015), Wroclaw (Poland), 1-8, May 25-27, 2015.
  

2014 (5)

• [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
  
• [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
  
• [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.
  
• [1809] M. Galar, A. Fernandez, E. Barrenechea, F. Herrera. Enhancing Difficult Classes in One-vs-One Classifier Fusion Strategy using Restricted Equivalence Functions. In Proceedings of the 17th International Conference on Information Fusion (FUSION'2014), SS07-DMKDIF, pp. 1-8 Salamanca (Spain), 7-10 July 2014.
  
• [1810] A. Fernandez, S. Río, V. López, A. Bawakid, M.J. del Jesus, J.M. Benítez, F. Herrera. Big Data with Cloud Computing: An Insight on the Computing Environment, MapReduce and Programming Frameworks. WIREs Data Mining and Knowledge Discovery 4:5 (2014) 380-409. doi: 10.1002/widm.1134
  

2013 (9)

• [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
  
• [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
  
• [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
  
• [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
  
  
• [1704] V. López, A. Fernandez, F. Herrera. Addressing Covariate Shift for Genetic Fuzzy Systems Classifiers: A Case of Study with FARC-HD for Imbalanced Datasets. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2013), Hyderabad (India), pp. 1-8, 7-10 July 2013.
  
• [1749] M. Galar, A. Fernandez, E. Barrenechea, H. Bustince, F. Herrera. Selección dinámica de clasificadores para la estrategia Uno-contra-Uno: Evitando los clasificadores no competentes. FINO 2013 (CEDI 2013), Madrid (España), pp. 1469-1478, 17-20 Septiembre 2013.
  
• [1750] M. Galar, A. Fernandez, E. Barrenechea, F. Herrera. Una debilidad de la estrategia Uno-contra-Uno en clasificación: Potenciando las clases difíciles. CAEPIA 2013 (CEDI 2013), Madrid (España), pp. 1333-1342, 17-20 Septiembre 2013.
  

2012 (10)

• [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
  
• [1451] J. Derrac, J. Luengo, A. Fernandez, S. García, J. Alcalá-Fdez. KEEL: Una herramienta docente para sistemas difusos. XVI Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2012), Valladolid (España), pp. 534-539, 1-3 Febrero.
  
• [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
  
• [1501] V. López, A. Fernandez, M.J. del Jesus, F. Herrera. Cost Sensitive and Preprocessing for Classification with Imbalanced Data-sets: Similar Behaviour and Potential Hybridizations. 1st International Conference on Pattern Recognition Applications and Methods (ICPRAM2012), Vilamoura (Portugal), pp. 98-107, 6-8 February 2012.
  
• [1500] V. López, A. Fernandez, M.J. del Jesus, F. Herrera. Un sistema de clasificación basado en reglas difusas jerárquico con programación genética para problemas de clasificación altamente no balanceados. XVI Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2012), Valladolid (Spain), pp. 313-318, 1-3 February 2012.
  
• [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
  
• [1532] A. Fernandez, F. Herrera. Linguistic Fuzzy Rules in Data Mining: Follow-Up Mamdani Fuzzy Modeling Principle. In: Enric Trillas, Piero P. Bonissone, Luis Magdalena and Janusz Kacprzyk (eds.): Combining Experimentation and Theory A Hommage to Abe Mamdani. Studies in Fuzziness and Soft Computing 271, (2012) 103-122.
  
• [1545] A. Fernandez, S. Río, F. Herrera, J.M. Benítez. An Overview on the Structure and Applications for Business Intelligence and Data Mining in Cloud Computing. 7th International Conference on Knowledge Management in Organizations: Service and Cloud Computing (KMO'12), Salamanca (Spain), pp. 559-570, 11-13 July 2012.
  
• [1546] A. Fernandez, D. Peralta, F. Herrera, J.M. Benítez. An Overview of E-Learning in Cloud Computing. Workshop on Learning Technology for Education in Cloud (LTEC'12), Salamanca (Spain), pp. 35-46, 11-13 Julio 2012.
  

2011 (11)

• [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
  
  
• [1345] A. Fernandez, V. López, M.J. del Jesus, F. Herrera. On the Usefulness of Fuzzy Rule Based Systems based on Hierarchical Linguistic Fuzzy Partitions. In: W. Pedrycz, S.-M. Chen (Eds.) Granular Computing and Intelligent Systems: Design with Information Granules of Higher Order and Higher Type, ISRL 13, Springer (2011), pp. 155-184, ISBN: 978-3-642-19819-9.
  
• [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
  
• [1385] A. Fernandez, S. García, F. Herrera. Addressing the Classication with Imbalanced Data: Open Problems and New Challenges on Class Distribution. 6th International Conference on Hybrid Artificial Intelligence Systems (HAIS2011). Wroclaw, Poland, 23-25 May 2011, pp. 1-10.
  
• [1386] J. Sanz, A. Fernandez, M. Pagola, A. Brugos, H. Bustince, F. Herrera. A Case Study on Medical Diagnosis of Cardiovascular Diseases Using a Genetic Algorithm for Tuning Fuzzy Rule-Based Classification Systems with Interval-Valued Fuzzy Sets. T2FUZZ - 2011 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems (IEEE-SSCI 2011) pp. 9-15, Paris, France 11-15 April 2011.
  
• [1420] J. Sanz, A. Fernandez, H. Bustince, F. Herrera. On the cooperation of Interval-Valued Fuzzy Sets and Genetic Tuning to Improve the Performance of Fuzzy Decision Trees. 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), pp. 1247-1254, Taipei (Taiwan) 27-30 June 2011.
  
• [1421] P. Villar, A. Fernandez, F. Herrera. Studying the Behaviour of a Multiobjective Genetic Algorithm to Design Fuzzy Rule-Based Classification Systems for Imbalanced Data-sets. 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), pp. 1239-1246, Taipei (Taiwan) 27-30 June 2011.
  
• [1440] J. Derrac, J. Luengo, J. Alcalá-Fdez, A. Fernandez, S. García. Using KEEL Software as a Educational Tool: A Case of Study Teaching Data Mining. Second International Conference on EUropean Transnational Education (ICEUTE 2011), Salamanca (Spain), pp. 55-60, October 20-21, 2011.
  
• [1484] E. Barrenechea, A. Fernandez, H. Bustince, F. Herrera. Construction of Interval-valued Fuzzy Preference Relations using Ignorance Functions. Interval-valued non dominance Criterion. Workshop on Fuzzy Methods for Knowledge-Based Systems, Eurofuse 2011. Advances in Intelligent and Soft Computing ASIC 107, Régua, Portugal, 21-23 Septiembre pp. 243-255 (2011).
  

2010 (15)

• [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
  
  
• [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
  
  
• [1250] J. Derrac, A. Fernandez, J. Luengo, S. García, L. Sánchez, J. Alcalá-Fdez, F. Herrera. KEEL: Una herramienta software para el análisis de sistemas difusos evolutivos. XV Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2010), Huelva (Spain), 417-422, 3-5 February 2010..
  
• [1255] A. Fernandez, M. Calderón, E. Barrenechea, H. Bustince, F. Herrera. Resolución de Problemas Multi-clase con Sistemas de Clasificación Basados en Reglas Difusas Lingüísticos Basados en Aprendizaje por Parejas y Relaciones de Preferencia. XV Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2010), Huelva (Spain), 97-102, 3-5 February 2010.
  
• [1256] J.A. Sanz, A. Fernandez, H. Bustince, F. Herrera. Un algoritmo genético para el ajuste de Sistemas de Clasificación Basados en Reglas Difusas con Conjuntos Intervalo-Valorados Difusos. XV Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2010), Huelva (Spain), 115-120, 3-5 February 2010.
  
• [1257] A. Fernandez. Sistemas de Clasificación Basados en Reglas Difusas Lingüísticas Aplicadas a Problemas con Clases No Balanceadas. Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada. March 2010.
  Advisor: F. Herrera and M.J. del Jesus
  
• [1258] A. Fernandez, M.J. del Jesus, F. Herrera. Analysing the Hierarchical Fuzzy Rule Based Classification Systems with Genetic Rule Selection. Proc. of the 4th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS2010) 69-74, Mieres (Spain) 17-19 March 2010.
  
• [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
  
• [1281] A. Fernandez, M.J. del Jesus, F. Herrera. Multi-class Imbalanced Data-Sets with Linguistic Fuzzy Rule Based Classification Systems Based on Pairwise Learning. 13th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU2010) Dortmund (Germany), LNAI 6178 pp 89-98, 28 June - 02 July 2010.
  
• [1282] P. Villar, A. Fernandez, F. Herrera. A Genetic Algorithm for Feature Selection and Granularity Learning in Fuzzy Rule-Based Classification Systems for Highly Imbalanced Data-Sets. 13th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU2010) Dortmund (Germany), CCIS 80 pp. 741-750, 28 June - 02 July 2010.
  
• [1294] J.A. Sanz, A. Fernandez, H. Bustince, F. Herrera. A Genetic Algorithm for Tuning Fuzzy Rule-Based Classification Systems with Interval-Valued Fuzzy Sets. In Proceedings on the WCCI 2010 IEEE World Congress on Computational Intelligence, IEEE Congress on Fuzzy Logic FUZZ-IEEE'2010, Barcelona (Spain), 18-23 July, pp 199-206.
  
• [1322] V. López, A. Fernandez, F. Herrera. Un primer estudio sobre el uso de aprendizaje sensible al coste con sistemas de clasificación basados en reglas difusas para problemas no balanceados. III Congreso Español de Informática (CEDI 2010). III Simposio sobre Lógica Fuzzy and Soft Computing (LFSC 2010), Valencia (Spain), pp. 459-466, 7-10 September 2010.
  
• [1340] V. López, A. Fernandez, F. Herrera. A First Approach for Cost-Sensitive Classification with Linguistic Genetic Fuzzy Systems in Imbalanced Data-sets. 10th International Conference on Intelligent Systems Design and Applications (ISDA2010), El Cairo (Egypt), pp. 676-681, 29th November - 1st December 2010.
  

2009 (15)

• [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
  
  
• [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
  
• [1025] S. García, A. Fernandez, F. Herrera. Un Primer Estudio sobre la Utilización de Selección Evolutiva de Conjuntos de Entrenamiento en Problemas de Clasificación con Clases no Balanceadas y Árboles de Decisión. In Proceedings of VI Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB'09), Málaga (Spain), 183-190, 11-13 February 2009.
  
• [1035] J. Sanz, A. Fernandez, H. Bustince, F. Herrera. A First Study on the Use of Interval-Valued Fuzzy Sets with Genetic Tuning for Classification with Imbalanced Data-sets. In Proceedings of the Fourth International Conference on Hybrid Artificial Intelligence Systems (HAIS 2009), June 10-12, Salamanca (Spain), Lecture Notes in Artificial Intelligence 5572, 581-588.
  
• [1036] A. Fernandez, M.J. del Jesus, F. Herrera. Improving the Performance of Fuzzy Rule Based Classification Systems for Highly Imbalanced Data-sets Using an Evolutionary Adaptive Inference System. 10th International Work-Conference on Artificial Neural Networks (IWANN09), Lecture Notes on Computer Science 5517, Salamanca (Spain), 294-301, June 2009.
  
• [1037] A. Fernandez, F.J. Berlanga, M.J. del Jesus, F. Herrera. Genetic Cooperative-Competitive Fuzzy Rule Based Learning Method using Genetic Programming for Highly Imbalanced Data-Sets. 13th International Fuzzy Systems Association World Congress and 6th European Society for Fuzzy Logic and Tecnology Conference (IFSA-EUSFLAT 2009) 42-47, Lisbon (Portugal), 20-24 July 2009.
  
• [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
  
• [1048] P. Villar, F. Herrera, A. Fernandez. A Genetic Learning of the Fuzzy Rule-Based Classification System Granularity for Highly Imbalanced Data-Sets. 2009 IEEE International Conference on Fuzzy Systems (Fuzz-IEEE09) Jeju Island, (Korea), 1689-1694, 20-24 August 2009.
  
• [1059] A. Fernandez, M. Calderón, E. Barrenechea, H. Bustince F. Herrera. Enhancing Fuzzy Rule Based Systems in Multi-Classification Using Pairwise Coupling with Preference Relations. EUROFUSE09 Workshop on Preference Modelling and Decision Analysis Pamplona (Spain), 39-46, 16-18 September 2009.
  
• [1061] M. D. Pérez-Godoy, A. J. Rivera, A. Fernandez, M. J. del Jesus and F. Herrera. A Preliminar Analysis of CO2RBFN in Imbalanced Problems. 10th International Work-Conference on Artificial Neural Networks (IWANN09) Lecture Notes on Computer Science 5517, Springer-Verlag, Salamanca (Spain) 57-64, June 2009.
  
• [1063] J. Sanz, M. Pagola, H. Bustince, A. Fernandez, F. Herrera. Construction of Ignorance Functions from Overlap Functions. EUROFUSE09 Workshop on Preference Modelling and Decision Analysis Pamplona (Spain), 317-322, 16-18 September 2009.
  
• [1078] A. Fernandez, J. Luengo, J. Derrac, J. Alcalá-Fdez and F. Herrera. Implementation and Integration of Algorithms into the KEEL Data-Mining Software Tool. 10th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2009). Lecture Notes in Computer Science 5788, Springer 2009, Burgos (Spain, 2009) 562-569.
  
• [1162] P. Villar, A. Fernandez, A.M. Sánchez, F. Herrera. Un Algoritmo Genético para Selección de Características en Sistemas de Clasificación Basados en Reglas Difusas para conjuntos de datos altamente no balanceados. Actas de la XIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA09) Sevilla (Spain), 519-528, 9-13 November 2009.
  
• [1176] J. Luengo, A. Fernandez, F. Herrera, S. García. Addressing Data-Complexity for Imbalanced Data-sets: A Preliminary Study on the Use of Preprocessing for C4.5. 9th International Conference on Intelligent Systems Designs and Applications (ISDA'09), Pisa (Italy) November 2009, 523-528 .
  

2008 (4)

• [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
  
• [0773] J. Alcalá-Fdez, S. García, F.J. Berlanga, A. Fernandez, L. Sánchez, M.J. del Jesus, F. Herrera. KEEL: A Data Mining Software Tool Integrating Genetic Fuzzy Systems. 3rd International Workshop on Genetic and Evolving Fuzzy Systems (GEFS 2008), Witten-Bommerholz (Germany), 83-88, 4-7 March 2008.
  
• [0845] A. Fernandez, M.J. del Jesus, F. Herrera. Sistemas Basados en Reglas Difusas en Clasificación: Nuevos Retos. XIV Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF08) Mieres (Spain), 493-500, 17-19 September 2008.
  
• [0844] A. Fernandez, M.J. del Jesus, F. Herrera. A Short Study on the Use of Genetic 2-Tuples Tuning for Fuzzy Rule Based Classification Systems in Imbalanced Data-Sets. 8th International Conference on Hybrid Intelligent Systems (HIS 2008) Barcelona (Spain), 483-488, 10-12 September.
  

2007 (3)

• [0664] A. Fernandez, S. García, M.J. del Jesus, F. Herrera. A study on the Use of the Fuzzy Reasoning Method based on the Winning Rule Vs. Voting Procedure for Classification with Imbalanced Data Sets. Proceedings of the 9th International Work-Conference on Artificial Neural Networks (IWANN07). Lecture Notes on Computer Science 4507, Springer-Verlag, San Sebastián (Spain), 375-382, June 2007.
  
• [0665] A. Fernandez, S. García, M.J. del Jesus, F. Herrera. An Analysis of the Rule Weights and Fuzzy Reasoning Methods for Linguistic Rule Based Classification Systems Applied to Problems with Highly Imbalanced Data Sets. International Workshop on Fuzzy Logic and Applications (WILF07). Lecture Notes in Computer Science 4578, Springer-Verlag 2007, Genova (Italy, 2007), 170-179.
  
• [0723] S. García, A. Fernandez, A.D. Benítez, F. Herrera. Statistical Comparisons by Means of Non-Parametric Tests: A Case Study on Genetic Based Machine Learning. Proceedings of the II Congreso Español de Informática (CEDI 2007). V Taller Nacional de Minería de Datos y Aprendizaje (TAMIDA 2007), Zaragoza (Spain), 95-104, 11-14 September 2007.
  

2006 (4)

• [0611] M.J. del Jesus, A. Fernandez, S. García, F. Herrera. A first study on the use of fuzzy rule based classification systems for problems with imbalanced data sets. Proceedings of the Symposium on Fuzzy Systems in Computer Science (FSCS 2006), Magdeburg (Germany), 63-72, 27-28 September 2006.
  
• [0612] A. Fernandez, S. García, F. Herrera, M.J. del Jesus. Un primer estudio sobre el uso de los sistemas de clasificación basados en reglas difusas en problemas de clasificación con clases no balanceadas. Proceedings of the XIII Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2006), Ciudad Real (Spain), 89-95, 20-22 September 2006, ISBN 84-689-9547-9.
  
• [0614] S. García, J.R. Cano, A. Fernandez, F. Herrera. A proposal of evolutionary prototype selection for class imbalance problems. Proceedings of the 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL06). Lecture Notes in Computer Science 4224, Springer-Verlag 2006, Burgos (Spain) 1415-1423, September 2006.
  
• [0643] E. Herrera-Viedma, J. López-Gijón, F. Herranz Navarra, J. Vilchez Pardo, A. Fernandez Porcel, S. Alonso. Una Herramienta para la Evaluación de la Calidad de las Bibliotecas Universitarias. Proceedings of the 4th International Symposium on Digital Libraries, Málaga (Spain), 270-281, 21-23 June 2006.