Papers published in Journals (S. García)

Number of Results: 103

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2022 (1)

• [3075] G. González-Almagro, J.L. Suárez, J. Luengo, J.R. Cano, S. García. 3SHACC: Three stages hybrid agglomerative constrained clustering. Neurocomputing 490: 441-461 (2022). doi: 10.1016/j.neucom.2021.12.018
  

2021 (4)

• [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
  
• [3077] G. González-Almagro, J. Luengo, J.R. Cano, S. García. Enhancing instance-level constrained clustering through differential evolution. Applied Soft Computing 108, 107435. doi: 10.1016/j.asoc.2021.107435
  
• [3079] M. González, J. Luengo, J. R. Cano, S. García. Synthetic Sample Generation for Label Distribution Learnin. Information Sciences 544: 197-213 (2021). doi: 10.1016/j.ins.2020.07.071
  
• [3081] G. González-Almagro, A. Rosales-Pérez, J. Luengo, J.R. Cano, S. García. ME-MEOA/DCC: Multiobjective constrained clustering through decomposition-based memetic elitism. Swarm Evolutionary Compututation 66: 100939 (2021).
  

2020 (5)

• [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
  
• [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
  
• [3082] G. González-Almagro, J. Luengo, J. R. Cano, S. García. DILS: Constrained clustering through dual iterative local search. Computers & Operations Research 121: 104979 (2020). doi: 10.1016/j.cor.2020.104979
  
• [3083] J. A. Cortés-Ibáñez, S. González, J. J. Valle-Alonso, J. Luengo, S. García, F. Herrera. Preprocessing methodology for time series: An industrial world application case study. Inf. Sci. 514: 385-401 (2020). doi: j.ins.2019.11.027
  

2019 (6)

• [2543] I. Triguero, D. García-Gil, J. Maillo, J. Luengo, S. García, F. Herrera. Transforming big data into smart data: An insight on the use of the k nearest neighbors algorithm to obtain quality data. Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery. e1289. doi: 10.1002/widm.1289
  
• [2557] D. García-Gil, J. Luengo, S. García, F. Herrera. Enabling smart data: noise filtering in big data classification. Information Sciences 479, 135-152. doi: 10.1016/j.ins.2018.12.002
  
• [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
  
• [2667] JR. Cano, J. Luengo, S. García. Label Noise Filtering Techniques to Improve Monotonic Classification. Neurocomputing 353: 83-95 (2019). doi: 10.1016/j.neucom.2018.05.131
  
• [3088] D. García-Gil, F. Luque Sánchez, J. Luengo, S. García, F. Herrera. From Big to Smart Data: Iterative ensemble filter for noise filtering in Big Data classification. International Journal of Intelligent Systems 34(12): 3260-3274 (2019). doi: 10.1002/int.22193
  
• [3089] I. Cordón, J. Luengo, S. García, F. Herrera, F. Charte. Smartdata: Data preprocessing to achieve smart data in R. Neurocomputing 360: 1-13 (2019). doi: 10.1016/j.neucom.2019.06.006
  

2018 (13)

• [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
  
• [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
  
• [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
  
• [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
  
• [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
  
• [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
  
• [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
  

2017 (16)

• [2307] J. Alcalá-Fdez, R. Alcalá, S. González, Y. Nojima, S. García. Evolutionary Fuzzy Rule-Based Methods for Monotonic Classification. IEEE Transactions on Fuzzy Systems 25:6 (2017) 1376-1390. doi: 10.1109/TFUZZ.2017.2718491
  
• [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
  
  
• [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
  
• [2155] J.R. Cano, N.R. Aljohani, R.A. Abbasi, J.S. Alowidbi, S. García. Prototype selection to improve monotonic nearest neighbor. Engineering Applications of Artificial Intelligence 60 (2017) 128-135. doi: 10.1016/j.engappai.2017.02.006
  
• [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
  
• [2157] M.A. Jarwar, R.A. Abbasi, M. Mushtaq, O. Maqbool, N.R. Aljohani, A. Daud, J.S. Alowibdi, J.R. Cano, S. García, I. Chong. CommuniMents: A Framework for Detecting Community Based Sentiments for Events. International Journal on Semantic Web and Information Systems (IJSWIS) 13:2 (2017) 87-108. doi: 10.4018/IJSWIS.2017040106
  
• [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
  
• [2180] J. García, H.M. Fardoun, D.M. Alghazzawi, J.R. Cano, S. García. MoNGEL: monotonic nested generalized exemplar learning. Pattern Analysis and Applications 20:2 (2017) 441–452. doi: 10.1007/s10044-015-0506-y
  
• [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.
  
• [2368] J. R. Cano, S. García. Training Set Selection for Monotonic Ordinal Classification. Data & Knowledge Engineering 112 (2017) 94-105. doi: 10.1016/j.datak.2017.10.003
  

2016 (11)

• [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
  
• [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
  
• [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
  
• [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
  
• [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
  
  
• [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
  
• [2161] S. Gutiérrez, S. García. Landmark-based music recognition system optimisation using genetic algorithms. Multimedia Tools and Applications 75:24 (2016) 16905-16922. doi: 10.1007/s11042-015-2963-0
  
• [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
  
• [2165] P.A. Gutiérrez, S. García. Current prospects on ordinal and monotonic classification. Progress in Artificial Intelligence 5:3 (2016), 171-179. doi: 10.1007/s13748-016-0088-y
  
• [2176] J. García, A.M. AlBar, N.R. Aljohani, J.R. Cano, S. García. Hyperrectangles selection for monotonic classification by using evolutionary algorithms. International Journal of Computational Intelligence Systems 9:1 (2016), 184-201. doi: 10.1080/18756891.2016.1146536
  

2015 (7)

• [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
  
  
• [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
  
• [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
  
  
• [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
  
  
• [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
  

2014 (5)

• [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
  
  
• [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
  
• [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
  
• [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
  
  
• [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 (3)

• [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
  
  
• [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
  
• [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 (8)

• [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
  
  
• [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
  
• [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
  
  
• [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
  
• [1710] C.J. Carmona, S. Ramírez-Gallego, F. Torres, E. Bernal, M.J. del Jesus, S. García. Web usage mining to improve the design of an e-commerce website: OrOliveSur.com . Expert Systems with Applications 39 (2012) 11243-11249. doi: 10.1016/j.eswa.2012.03.046
  

2011 (5)

• [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
  
  
• [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
  
• [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
  
• [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
  
  

2010 (7)

• [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
  
• [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
  
  
• [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
  
  
• [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
  
• [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
  
  

2009 (7)

• [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
  
  
• [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.
  
• [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.
  
• [0893] J. Luengo, S. García, F. Herrera. A Study on the Use of Statistical Tests for Experimentation with Neural Networks: Analysis of Parametric Test Conditions and Non-Parametric Tests. Expert Systems with Applications 36 (2009) 7798-7808. doi: 10.1016/j.eswa.2008.11.041
  COMPLEMENTARY MATERIAL to the paper: Software and tests description
  
  
• [0898] S. García, A. Fernandez, J. Luengo, F. Herrera. A Study of Statistical Techniques and Performance Measures for Genetics-Based Machine Learning: Accuracy and Interpretability. Soft Computing 13:10 (2009) 959-977. doi: 10.1007/s00500-008-0392-y
  COMPLEMENTARY MATERIAL to the paper: Software and tests description
  
  
• [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 (5)

• [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
  
• [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
  
• [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