Isaac Triguero

Full list of publications

Number of Results: 68

Jump to Year: 2019, 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010

2019 (2)

• R. Tickle, I. Triguero, GP. Figueredo, M. Mesgarpour, R. John. PAS3-HSID: A Dynamic Bio-Inspired Approach for Real-time Hot Spot Identification in Data Streams. Cognitive Computation, in press, 2019.
• M. Jiménez, I. Triguero, R. John. Handling uncertainty in citizen science data: Towards an improved amateur-based large-scale classification. Information Sciences 479, 301-320. doi: 10.1016/j.ins.2018.12.011
  

2018 (11)

• M. Gonzalez, C. Bergmeir, I. Triguero, Y. Rodriguez, J.M. Benitez. Self-labeling techniques for semi-supervised time series classification: an empirical study. Knowledge and Information Systems, 55 (2), 493-528. doi: 10.1007/s10115-017-1090-9
  
• 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
  
• J. Maillo, J. Luengo, S. Garcia, F. Herrera, I. Triguero. A preliminary study on Hybrid Spill-Tree Fuzzy k-Nearest Neighbors for big data classification. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2018), Rio de Janeiro (Brazil), July 8-13. doi: 10.1109/FUZZ-IEEE.2018.8491595
  
• J. Maillo, J. Luengo, S. Garcia, F. Herrera, I. Triguero.. Un enfoque aproximado para acelerar el algoritmo de clasificacion Fuzzy kNN para Big Data. II Workshop en Big Data y Análisis de Datos Escalable (BigDADE 2018), Granada (España), 23-26 octubre 2018.
  
• 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
  
• 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
  
• JS. Angarita-Zapata, I. Triguero, AD. Masegosa. A Preliminary Study on Automatic Algorithm Selection for Short-Term Traffic Forecasting. International Symposium on Intelligent and Distributed Computing, 204-214.
  
• W. Ding, I. Triguero, CT. Lin. Coevolutionary Fuzzy Attribute Order Reduction With Complete Attribute-Value Space Tree. IEEE Transactions on Emerging Topics in Computational Intelligence. doi: 10.1109/TETCI.2018.2869919
  
• M. Galar, I. Triguero, H. Bustince, F. Herrera. A Preliminary Study of the Feasibility of Global Evolutionary Feature Selection for Big Datasets under Apache Spark. IEEE Congress on Evolutionary Computation (CEC), 1-8. Rio de Janeiro (Brazil), July, 8-13, 2018. doi: 10.1109/CEC.2018.8477878
  
• N. Xue, D. Landa-Silva, I. Triguero, GP. Figueredo. A genetic algorithm with composite chromosome for shift assignment of part-time employees. IEEE Congress on Evolutionary Computation (CEC), 1-8, Rio de Janeiro (Brazil), 8-13 July, 2018.
  
• M. Jiménez, I. Triguero, R. John. A first approach for handling uncertainty in citizen science. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). July 8-13, Rio de Janeiro (Brazil), 2018.
  

2017 (8)

• 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.
• M. Gonzalez, C. Bergmeir, I. Triguero, Y. Rodriguez, J.M. Benitez. Self-labeling techniques for semi-supervised time series classification: an empirical study. Knowledge and Information Systems, in press. doi: 10.1007/s10115-017-1090-9
  
• G.P. Figueredo, I. Triguero, M. Mesgaspour, A.M. Guerra, J.M. Garibaldi, R. I. John. Detecting danger in roads: an immune-inspired technique to identify heavy goods vehicles incident hot spots. IEEE Transactions on Emerging Topics in Computational Intelligence, in press
• J. Maillo, S. Ramírez-Gallego, I. Triguero, F. Herrera. kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data. Knowledge-Based Systems 117 (2017) 3-15. doi: 10.1016/j.knosys.2016.06.012
  
• 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
  
• I. Triguero, M. Galar, H. Bustince, F. Herrera. A First Attempt on Global Evolutionary Undersampling for Imbalanced Big Data. IEEE Congress on Evolutionary Computation (CEC 2017), San Sebastian (Spain), 2054-2061, June 5-8.
• J. Maillo, J. Luengo, S. Garcia, F. Herrera, I. Triguero. Exact Fuzzy k-Nearest Neighbor Classification for Big Datasets. IEEE Conference on Fuzzy Systems (FUZZ-IEEE 2017), Naples (Italy), July 9-12.
  
• I. Triguero, G.P. Figueredo, M. Mesgaspour, J.M. Garibaldi, R. I. John. Vehicle Incident Hot Spots Identification: An Approach for Big Data. The 11th IEEE International Conference On Big Data Science And Engineering (IEEE BigDataSE-17) Sydney, Australia, August 1-4, 2017

2016 (6)

• M. González, C. Bergmeir, I. Triguero, Y. Rodríguez, J.M. Benítez. On the Stopping Criteria for k-Nearest Neighbor in Positive Unlabeled Time Series Classification Problems. Information Sciences 328 (2016) 42-59. doi: 10.1016/j.ins.2015.07.061
  
• S. Vluymans, I. Triguero, C. Cornelis, Y. Saeys. EPRENNID: An Evolutionary Prototype Reduction Based Ensemble for Nearest Neighbor Classification of Imbalanced Data. Neurocomputing 216 (2016) 596-610. doi: 10.1016/j.neucom.2016.08.026
  
• I. Triguero, C. Vens. Labelling Strategies for Hierarchical Multi-Label Classification Techniques. Pattern Recognition 56 (2016) 170-183. doi: 10.1016/j.patcog.2016.02.017
  
• 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
  
• I. Triguero, M. Galar, D. Merino, J. Maillo, H. Bustince, F. Herrera. Evolutionary Undersampling for Extremely Imbalanced Big Data Classification under Apache Spark. IEEE Congress on Evolutionary Computation (CEC 2016), Vancouver (Canada), 640-647, July 24-29
  
• I. Triguero, J. Maillo, J. Luengo, S. García, F. Herrera. From Big data to Smart Data with the K-Nearest Neighbours algorithm. The 2016 IEEE International Conference on Smart Data (SmartData 2016), Chengdu (China), Dec 16-19, 2016. doi: 10.1109/iThings-GreenCom-CPSCom-SmartData.2016.177

2015 (12)

• 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
  
  
• 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
  
• 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
  
• 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
  
• 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
  
  
• 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
  
  
• I. Triguero, S. Río, V. López, J. Bacardit, J.M. Benítez, F. Herrera. ROSEFW-RF: The winner algorithm for the ECBDL'14 Big Data Competition: An extremely imbalanced big data bioinformatics problem. Knowledge-Based Systems 87 (2015) 69-79. doi: 10.1016/j.knosys.2015.05.027
  
• I. Triguero, M. Galar, S. Vluymans, C. Cornelis, H. Bustince, F. Herrera, Y. Saeys. Evolutionary Undersampling for Imbalanced Big Data Classification. IEEE Congress on Evolutionary Computation (CEC 2015), Sendai (Japan), 715-722, May 25-28, 2015.
  
• D. Peralta, S. Río, S. Ramírez-Gallego, I. Triguero, J.M. Benítez, F. Herrera. Evolutionary Feature Selection for Big Data Classification: A MapReduce Approach. Mathematical Problems in Engineering, vol. 2015, Article ID 246139 (2015) 11 pages. doi: 10.1155/2015/246139
  
• J. Maillo, I. Triguero, F. Herrera. A MapReduce-based k-Nearest Neighbor Approach for Big Data Classification. 9th International Conference on Big Data Science and Engineering (IEEE BigDataSE-15), Helsinki (Finland), 167-172, August 20-22, 2015.. doi: 10.1109/Trustcom.2015.577
  
• D. Peralta, I. Triguero, Y. Saeys, S. García, J.M. Benítez, F. Herrera. Clasificación Jerárquica de Huellas Dactilares con Selección de Características. VII Symposium of Theory and Applications of Data Mining (TAMIDA), CAEPIA 2015, Albacete (España), pp. 831-840, 09-12 Noviembre 2015.
  
• J. Maillo, I. Triguero, F. Herrera. Un enfoque MapReduce del algoritmo de clasificación k-vecinos más cercanos para Big Data. 1er Workshop en Big Data y Análisis de Datos Escalable (BigDADE I), Albacete (España), 9-12 noviembre 2015.
  

2014 (8)

• 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
  
• 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
  
• D. Peralta, I. Triguero, R. Sanchez-Reillo, F. Herrera, J.M. Benítez. Fast Fingerprint Identification for Large Databases. Pattern Recognition 47:2 (2014) 588–602. doi: 10.1016/j.patcog.2013.08.002
  COMPLEMENTARY MATERIAL to the paper: datasets, experimental results and source codes
  
  
• D. Peralta, M. Galar, I. Triguero, O. Miguel-Hurtado, J.M. Benítez, F. Herrera. Minutiae Filtering to Improve Both Efficacy and Efficiency of Fingerprint Matching Algorithms. Engineering Applications of Artificial Intelligence, 32 (2014) 37-53. doi: 10.1016/j.engappai.2014.02.016
  
• D. Gomez-Lorente, I. Triguero, C. Gil, O. Rabaza. Multi-Objective Evolutionary Algorithms for the Design of Grid- Connected Solar Tracking Systems. International Journal of Electrical Power and Energy Systems, 61 (2014) 371-379. doi: 10.1016/j.ijepes.2014.03.064
  
• I. Triguero. Algoritmos evolutivos de codificación real para el problema de generación de prototipos en aprendizaje supervisado y semi-supervisado basado en instancias. PhD Dissertation, Department of Computer Science and Artificial Intelligence, University of Granada..
  Advisor: F. Herrera, S. García
  
• I. Triguero, D. Peralta, J. Bacardit, S. García, F. Herrera. A Combined MapReduce-Windowing Two-Level Parallel Scheme for Evolutionary Prototype Generation. In Proceeding on the WCCI 2014 IEEE World Congress on Computational Intelligence, IEEE Congress on Evolutionary Computation CEC'2014, Beijing (China), 6-11 July, pp. 3036-3043, 2014. doi: 10.1109/CEC.2014.6900490
  
• B. Krawczyk, I. Triguero, S. García, M. Wozniak, F. Herrera. A First Attempt on Evolutionary Prototype Reduction for Nearest Neighbor One-Class Classification.. In Proceeding on the WCCI 2014 IEEE World Congress on Computational Intelligence, IEEE Congress on Evolutionary Computation CEC'2014, Beijing (China), 6-11 July, pp 747-753, 2014.
  

2013 (2)

• D. Gómez-Lorente, I. Triguero, C. Gil, A. Espín Estrella. Algoritmos evolutivos multi-objetivo para el diseño de plantas fotovoltaicas con seguimiento solar. Actas del IX Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2013), Madrid (Spain), pp 249-258, 17-20 Septiembre, 2013.
  
• I. Triguero, S. García, F. Herrera. Tri-Training con sobremuestreo para aprendizaje semi-supervisado. Actas de la XV Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2013), Madrid (Spain), pp 149-158 , 17-20 Septiembre, 2013.
  

2012 (11)

• 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
  
  
• 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
  
• D. Gómez-Lorente, I. Triguero, C. Gil, A. Espín Estrella. Evolutionary Algorithms for the Design of Grid-connected PV-systems. Expert Systems with Applications 39:9 (2012) 8086-8094. doi: 10.1016/j.eswa.2012.01.159
  
• I. Triguero, J. Derrac, S. García, F. Herrera. Evolución Diferencial para Reducción de Prototipos y Ponderación de Características. Actas del VIII Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB12), Albacete (Spain), pp. 267-274, Febrero 8-10, 2012.
  
• P.D. Gutiérrez, I. Triguero, F. Herrera. Algoritmos Basados en Nubes de Partículas y Evolución Diferencial para el Problema de Optimización Continua: Un estudio experimental. Actas del VIII Congreso Español sobre Metaheurística, Algoritmos Evolutivos y Bioinspirados (MAEB12), Albacete (Spain), pp. 144-156, Febrero 8-10, 2012.
  
• 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
  
  
• J. Derrac, I. Triguero, S. García, F. Herrera. A Co-evolutionary Framework for Nearest Neighbor Enhancement: Combining Instance and Feature Weighting with Instance Selection. In Proceedings of the Seventh International Conference on Hybrid Artificial Intelligence Systems (HAIS 2012), March 28-30, Salamanca (Spain), Lecture Notes in Computer Science 7209, 176-187.
  
• 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
  
• C. Bergmeir, I. Triguero, F. Velasco, J.M. Benítez. Optimization of neuro-coefficient smooth transition autoregressive models using differential evolution. In Proceedings of the Seventh International Conference on Hybrid Artificial Intelligence Systems (HAIS 2012), March 28-30, Salamanca (Spain), Lecture Notes in Computer Science 7208, 464-473.
  
• C. Bergmeir, I. Triguero, D. Molina, J.L. Aznarte M., J.M. Benítez. Time Series Modeling and Forecasting Using Memetic Algorithms for Regime-switching Models. IEEE Transactions on Neural Networks and Learning Systems (2012), volume 23, issue 11, pages 1841-1847. doi: 10.1109/TNNLS.2012.2216898
  
• C. Bergmeir, I. Triguero, F. Velasco, J.M. Benítez. Optimización de Modelos Estadísticos y Difusos para el Análisis de Series Temporales Mediante Evolución Diferencial. Actas del XVI Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF2012), Valladolid (Spain), February 2012.
  

2011 (4)

• 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
  
• I. Triguero, S. García, F. Herrera. Enhancing IPADE Algorithm with a Different Individual Codification. 6th International Conference on Hybrid Artificial Intelligence Systems (HAIS2011). Wroclaw, Poland, 23-25 May 2011, LNAI 6679, pp. 262–270.
  
• I. Triguero, J. Derrac, S. García, F. Herrera. A Study of the Scaling up Capabilities of Stratified Prototype Generation. Third World Congress on Nature and Biologically Inspired Computing (NABIC'11), Salamanca (Spain), pp. 304-309, October 19-21, 2011.
  
• I. Triguero, J. Derrac, S. García, F. Herrera. Un esquema de Pesos basado en Evolución Diferencial para Generación de Prototipos. Actas de la XIV Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA11), Tenerife (Spain), November 7-11, 2011.
  

2010 (4)

• S. García, J. Derrac, I. Triguero, C. Carmona, F. Herrera. A Preliminary Study on the Selection of Generalized Instances for Imbalanced Classification. Twenty Third International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE 2010), Córdoba (Spain), Lecture Notes in Artificial Intelligence (LNAI) 6096, 601-610, , 1-4 June 2010.
  
• I. Triguero, S. García, F. Herrera. A preliminary study on the use of differential evolution for adjusting the position of examples in nearest neighbor classification. In Proceeding on the WCCI 2010 IEEE World Congress on Computational Intelligence, IEEE Congress on Evolutionary Computation CEC'2010, Barcelona (Spain), 18-23 July, pp 630-637, 2010.
  
• J. Derrac, I. Triguero, S. García, F. Herrera. Coevolución de selección de instancias y esquemas de pesos para clasificadores basados en la regla del vecino más cercano. In Proceedings of the III Congreso Español de Informática (CEDI 2010). VII Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados, MAEB2010, Valencia (Spain), 481-488, 7-10 September 2010.
  
• 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