Papers published in Journals (A. Fernandez)

Number of Results: 69

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2024 (1)

• [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
  

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 (7)

• [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
  

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
  

2019 (4)

• [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
  
• [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
  

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
  
• [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
  
• [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
  
• [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.
  

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
  
• [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.
  

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
  
• [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
  
• [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
  
• [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
  

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
  
• [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.
  
• [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 (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
  
• [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
  
  

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
  
• [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
  
• [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
  

2011 (4)

• [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
  
  
• [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
  

2010 (5)

• [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
  
  
• [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
  

2009 (4)

• [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
  
• [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
  

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