Isaac Triguero


I am an Associate Professor of Data Science at the School of Computer Science of the University of Nottingham. In my research, I aim to make fundamental advances in data science, specifically in data mining, data reduction, semi-supervised learning, extreme classification and big data learning. I am now very fascinated by the challenges associated with processing big amounts of data to extract valuable knowledge in extreme frameworks.

Python Book

1st Edition Thorsten Altenkirch and Isaac Triguero.
Self-published on Lulu.com,
30th September 2019,
Paperback: ISBN 978-0-244-82276-7;
260 pages

Sample » Paperback » eBook »

Contact

School of Computer Science, University of Nottingham.
Computation Optimisation and Learning Lab (COL)
Tel.: 0115 74 87415
E-Mail: Isaac.Triguero@removethis.nottingham.ac.uk
Address: Jubilee Campus, Wollaton Road,
Nottingham NG8 1BB, UK

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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 BibTex Icon


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 PDF Icon
  • 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 PDF Icon
  • 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. PDF Icon
  • 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. PDF Icon BibTex Icon
  • 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 PDF Icon
  • 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 BibTex Icon
  • 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. PDF Icon
  • 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)


2015 (12)


2014 (8)


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 PDF Icon
    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 PDF Icon
  • 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 PDF Icon
  • 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. PDF Icon
  • 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. PDF Icon
  • 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 PDF Icon
    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. PDF Icon
  • 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 PDF Icon
  • 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 PDF Icon
  • 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 PDF Icon
  • 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. PDF Icon
  • 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. PDF Icon
  • 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. PDF Icon


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. PDF Icon
  • 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. PDF Icon
  • 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. PDF Icon
  • 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 PDF Icon
    COMPLEMENTARY MATERIAL to the paper: datasets, experimental results and source codes