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Papers published in Journals (A. Fernandez)

Number of Results: 53

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In Press (1)

  • [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, in press. doi: 10.1109/TFUZZ.2018.2866967 PDF Icon


2019 (2)

  • [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 PDF Icon BibTex Icon
  • [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 PDF Icon BibTex Icon


2018 (6)

  • [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 PDF Icon BibTex Icon
  • [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 PDF Icon BibTex Icon
  • [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.5590 PDF Icon BibTex Icon
  • [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 PDF Icon BibTex Icon
  • [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 BibTex Icon
  • [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. BibTex Icon


2017 (6)

  • [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 PDF Icon BibTex Icon
  • [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 PDF Icon BibTex Icon
  • [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 PDF Icon BibTex Icon
  • [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 PDF Icon BibTex Icon
  • [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 PDF Icon BibTex Icon
  • [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. PDF Icon BibTex Icon


2016 (4)

  • [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 PDF Icon BibTex Icon
  • [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 PDF Icon BibTex Icon
  • [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 PDF Icon BibTex Icon
  • [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 (5)

  • [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 PDF Icon BibTex Icon
  • [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 PDF Icon BibTex Icon
  • [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 PDF Icon BibTex Icon
  • [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 PDF Icon BibTex Icon
  • [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 PDF Icon BibTex Icon


2014 (4)

  • [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 PDF Icon BibTex Icon
  • [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 PDF Icon BibTex Icon
  • [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. PDF Icon
  • [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 PDF Icon BibTex Icon


2013 (6)

  • [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 PDF Icon BibTex Icon
  • [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 PDF Icon BibTex Icon
  • [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 PDF Icon BibTex Icon
  • [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 PDF Icon BibTex Icon
  • [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 PDF Icon BibTex Icon
  • [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 PDF Icon BibTex Icon
    COMPLEMENTARY MATERIAL to the paper


2012 (4)

  • [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 PDF Icon
  • [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 PDF Icon
  • [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 PDF Icon
  • [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 PDF Icon


2011 (4)


2010 (5)


2009 (4)


2008 (1)

  • [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 PDF Icon


2004 (1)

  • [0959] J.F. Fernández-Sánchez, A. Segura, J.M. Ben�tez, C. Cruces-Blanco, A. Fernández-Gutiérrez. Fluorescence optosensor using an artificial neural network for screening of polycyclic aromatic hydrocarbons. Analytica Chimica Acta 510 (2004) 183–187. doi: 10.1016/j.aca.2004.01.012 PDF Icon