Isaac Triguero

Publications

148 publications, retrieved from my Google Scholar profile (September 2026). See Scholar for citation metrics and the most up-to-date list.

Jump to year: 2026, 2025, 2024, 2023, 2022, 2021, 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2012, 2011, 2010, Other

2026 (6)

• Predicting Wind Turbine Power Using Machine Learning and Weather Forecasts
  K Boodhoo, I Triguero, J Plumbly, B Nicolson, N Watson
  arXiv preprint arXiv:2609.06194
• Too much of a good thing--when knowledge distillation promotes overfitting, and how to avoid it
  I Trigueros-Lorca, L Concepción, C Wagner, I Triguero, D Molina
  arXiv preprint arXiv:2608.23752
• Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source
  AD Cencillo, L Concepción, J Luengo, I Triguero
  arXiv preprint arXiv:2605.31259
• LUCoS: Latent Unsupervised Context Selection for Tabular Foundation Models
  O Ipas, G Gomez-Trenado, R Romero-Zaliz, I Triguero
  arXiv preprint arXiv:2605.27254
• VACE: Learning Geometrically Structured Representations for Time Series Anomaly Detection
  AD Cencillo, L Concepción, I Triguero, J Luengo
  arXiv preprint arXiv:2605.23504
• Exploring Representation Learning for Developmental Infant EEG
  J Uclés, K Stemikovskaya, LM Cómbita, MÁ Ballesteros-Duperón et al.
  2026 IEEE Conference on Artificial Intelligence (CAI), 2058-2063

2025 (12)

• Predict+ Optimize Problem in Renewable Energy Scheduling.
  C Bergmeir, F De Nijs, E Genov, A Sriramulu, M Abolghasemi, R Bean et al.
  IEEE Access Cited by 25
• Evolutionary computation for the design and enrichment of general-purpose artificial intelligence systems: Survey and prospects
  D Molina, J Poyatos, J Del Ser, S Garcia, H Ishibuchi, I Triguero, B Xue et al.
  IEEE Transactions on Evolutionary Computation Cited by 11
• Decision-focused learning enhanced by automated feature engineering for energy storage optimisation
  N Alkhulaifi, IG Dogan, TR Cargan, AL Bowler, D Pekaslan, NJ Watson et al.
  Expert Systems with Applications, 130554 Cited by 9
• AutoEnergy: An automated feature engineering algorithm for energy consumption forecasting with AutoML
  N Alkhulaifi, AL Bowler, D Pekaslan, NJ Watson, I Triguero
  Knowledge-Based Systems, 114300 Cited by 6
• A first approach to refine semantic spaces in zero-shot learning with a genetic algorithm
  JJ Herrera, F Herrera, I Triguero
  2025 IEEE Congress on Evolutionary Computation (CEC), 1-4 Cited by 2
• Generalising Stock Detection in Retail Cabinets with Minimal Data Using a DenseNet and Vision Transformer Ensemble
  B Rahi, D Sagmanli, F Oppong, D Pekaslan, I Triguero
  Machine Learning and Knowledge Extraction 7 (3), 66 Cited by 1
• A Case for the Use of Chroma Cartesian Colour Representations for Image Classification on Plant-Based Domains
  AJS Payne, G Hopkins, SN Gowda, I Triguero, MP Pound
  Proceedings of the IEEE/CVF International Conference on Computer Vision … Cited by 1
• Monte Carlo-Based Interval TOPSIS for Navigating Decision Support Under Uncertainty
  J Ying, C Wagner, I Triguero, S Kabir
  International Conference on Intelligent Data Engineering and Automated …
• From Traditional Methods to GPT-based Models for 2D Video Game Level Procedural Content Generation: An Empirical Study
  D Cerezo, I Triguero
  2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC …
• Semantic-Inductive Attribute Selection for Zero-Shot Learning
  JJ Herrera-Aranda, G Gomez-Trenado, F Herrera, I Triguero
  arXiv preprint arXiv:2510.03260
• Frequency-Aware Contrastive Loss for Self-Supervised EEG Seizure Detection
  K Stemikovskaya, D Manjarrés, I Triguero
  2025 International Joint Conference on Neural Networks (IJCNN), 1-8
• Directed Perturbations for Efficient Learning of Surrogate Losses
  TR Cargan, D Landa-Silva, I Triguero
  2025 International Joint Conference on Neural Networks (IJCNN), 1-7

2024 (11)

• General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance
  I Triguero, D Molina, J Poyatos, J Del Ser, F Herrera
  Information Fusion 103, 102135 Cited by 172
• Cluster analysis of blood biomarkers to identify molecular patterns in pulmonary fibrosis: assessment of a multicentre, prospective, observational cohort with independent …
  HP Fainberg, Y Moodley, I Triguero, TJ Corte, JMB Sand, DJ Leeming et al.
  The Lancet Respiratory Medicine 12 (9), 681-692 Cited by 47
• SEGAL time series classification—Stable explanations using a generative model and an adaptive weighting method for LIME
  H Meng, C Wagner, I Triguero
  Neural Networks 176, 106345 Cited by 37
• Machine learning pipeline for energy and environmental prediction in cold storage facilities
  N Alkhulaifi, AL Bowler, D Pekaslan, G Serdaroglu, S Closs, NJ Watson et al.
  IEEE Access 12, 153935-153951 Cited by 11
• Local-global methods for generalised solar irradiance forecasting: TR Cargan et al.
  TR Cargan, D Landa-Silva, I Triguero
  Applied Intelligence 54 (2), 2225-2247 Cited by 8
• Exploring automated feature engineering for energy consumption forecasting with autoML
  N Alkhulaifi, AL Bowler, D Pekaslan, I Triguero, NJ Watson
  2024 IEEE International conference on systems, man, and cybernetics (SMC … Cited by 6
• A wearable eye-tracking approach for early autism detection with machine learning: unravelling challenges and opportunities
  J Lopez-Martinez, P Checa, JM Soto-Hidalgo, I Triguero, A Fernández
  2024 International Joint Conference on Neural Networks (IJCNN), 1-8 Cited by 6
• A preliminary study on preprocessing the semantic space in zero-shot learning
  JJH Aranda, F Herrera, I Triguero
  International Conference on Hybrid Artificial Intelligence Systems, 177-189 Cited by 2
• Semi-supervised predictive clustering trees for multi-label protein subcellular localization
  LU Alcantara, I Triguero, R Cerri
  Brazilian Conference on Intelligent Systems, 384-399 Cited by 1
• Human Perceptions of Novel Visual and Non-Visual Explanations in High-Stakes Decision-Making Domains
  EB Abam, H Webb, L Dowthwaite, I Triguero
  International Conference on Human-Computer Interaction, 3-13 Cited by 1
• Classification of Patients With Idiopathic Pulmonary Fibrosis According to Blood Biomarker Signatures by Consensus Cluster Analysis: A Multiple Machine Learning Approach
  H Fainberg, YP Moodley, I Triguero, T Corte, JMB Sand, DJ Leeming et al.
  American Thoracic Society International Conference Meetings Abstracts 209 …

2023 (8)

• Explaining time series classifiers through meaningful perturbation and optimisation
  H Meng, C Wagner, I Triguero
  Information Sciences 645, 119334 Cited by 34
• Identifying bird species by their calls in Soundscapes
  K Maclean, I Triguero
  Applied Intelligence 53 (19), 21485-21499 Cited by 17
• An initial step towards stable explanations for multivariate time series classifiers with lime
  H Meng, C Wagner, I Triguero
  2023 IEEE International Conference on Fuzzy Systems (FUZZ), 1-6 Cited by 11
• CzSL: Learning from citizen science, experts, and unlabelled data in astronomical image classification
  M Jiménez, EJ Alfaro, M Torres Torres, I Triguero
  Monthly Notices of the Royal Astronomical Society 526 (2), 1742-1756 Cited by 7
• AutoEn: an AutoML method based on ensembles of predefined machine learning pipelines for supervised traffic forecasting
  JS Angarita-Zapata, AD Masegosa, I Triguero
  arXiv preprint arXiv:2303.10732 Cited by 4
• Large-Scale Data Analytics with Python and Spark: A Hands-on Guide to Implementing Machine Learning Solutions
  I Triguero, M Galar
  Cambridge University Press Cited by 3
• The energy prediction smart-meter dataset: Analysis of previous competitions and beyond
  D Pekaslan, JM Alonso-Moral, K Bandara, C Bergmeir et al.
  arXiv preprint arXiv:2311.04007 Cited by 3
• Hyper-Stacked: Scalable and Distributed Approach to AutoML for Big Data
  R Dave, JS Angarita-Zapata, I Triguero
  International Cross-Domain Conference for Machine Learning and Knowledge …

2022 (7)

• Forced vital capacity trajectories in patients with idiopathic pulmonary fibrosis: a secondary analysis of a multicentre, prospective, observational cohort
  HP Fainberg, JM Oldham, PL Molyneaux, RJ Allen, LM Kraven, WA Fahy et al.
  The Lancet Digital Health 4 (12), e862-e872 Cited by 45
• A fusion spatial attention approach for few-shot learning
  H Song, B Deng, M Pound, E Özcan, I Triguero
  Information Fusion 81, 187-202 Cited by 33
• SPMS-ALS: A Single-Point Memetic structure with accelerated local search for instance reduction
  HL Le, F Neri, I Triguero
  Swarm and Evolutionary Computation 69, 100991 Cited by 9
• Feature importance identification for time series classifiers
  H Meng, C Wagner, I Triguero
  2022 IEEE international conference on systems, man, and cybernetics (SMC … Cited by 6
• Accelerated pattern search with variable solution size for simultaneous instance selection and generation
  HL Le, F Neri, D Landa-Silva, I Triguero
  Proceedings of the Genetic and Evolutionary Computation Conference Companion … Cited by 2
• Citizen Science and Machine Learning: Towards a Robust Large-Scale Automatic Classification in Astronomy
  M Jiménez, EJ Alfaro, I Triguero
  ML4Astro International Conference, 145-148
• Analysis of Forced Vital Capacity (FVC) Trajectories in Idiopathic Pulmonary Fibrosis (IPF) Identifies Four Distinct Clusters of Disease Behaviour
  H Fainberg, J Oldham, P Molyneaux, R Allen, L Kraven, W Fahy, J Porte et al.

2021 (8)

• EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification
  HL Le, D Landa-Silva, M Galar, S Garcia, I Triguero
  Applied Soft Computing 101, 107033 Cited by 43
• Beyond global and local multi-target learning
  M Basgalupp, R Cerri, L Schietgat, I Triguero, C Vens
  Information Sciences 579, 508-524 Cited by 20
• Decomposition-fusion for label distribution learning
  M González, G González-Almagro, I Triguero, JR Cano, S García
  Information Fusion 66, 64-75 Cited by 18
• L2ae-d: Learning to aggregate embeddings for few-shot learning with meta-level dropout
  H Song, MT Torres, E Özcan, I Triguero
  Neurocomputing 442, 200-208 Cited by 12
• FUZZ-IEEE Competition on Explainable Energy Prediction
  I Triguero, JM Alonso, L Magdalena, C Wagner, J Bernabé-Moreno Cited by 4
• ssc: An R Package for Semi-Supervised Classification
  M González, O Rosado, JD Rodríguez, C Bergmeir, I Triguero, JM Benítez
  R package version 2021, 21-0 Cited by 4
• Few-shot learning for postnatal gestational age estimation
  S Romanov, H Song, M Valstar, D Sharkey, C Henry, I Triguero et al.
  2021 International Joint Conference on Neural Networks (IJCNN), 1-8 Cited by 2
• Neurocomputing guest editorial for the special issue: Advances in deep and shallow machine learning approaches for handling data irregularities
  S Das, S Garcia, I Triguero Cited by 1

2020 (9)

• Multigranulation supertrust model for attribute reduction
  W Ding, W Pedrycz, I Triguero, Z Cao, CT Lin
  IEEE Transactions on Fuzzy Systems 29 (6), 1395-1408 Cited by 78
• Redundancy and complexity metrics for big data classification: towards smart data
  J Maillo, I Triguero, F Herrera
  IEEE Access 8, 87918-87928 Cited by 54
• Galaxy image classification based on citizen science data: A comparative study
  M Jimenez, MT Torres, R John, I Triguero
  IEEE Access 8, 47232-47246 Cited by 42
• General-purpose automated machine learning for transportation: A case study of auto-sklearn for traffic forecasting
  JS Angarita-Zapata, AD Masegosa, I Triguero
  International Conference on Information Processing and Management of … Cited by 25
• A hybrid surrogate model for evolutionary undersampling in imbalanced classification
  HL Le, D Landa-Silva, M Galar, S Garcia, I Triguero
  2020 IEEE Congress on Evolutionary Computation (CEC), 1-8 Cited by 13
• Chi-BD-DRF: design of scalable fuzzy classifiers for big data via a dynamic rule filtering approach
  F Aghaeipoor, MM Javidi, I Triguero, A Fernández
  2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-7 Cited by 11
• Evaluating automated machine learning on supervised regression traffic forecasting problems
  JS Angarita-Zapata, AD Masegosa, I Triguero
  Computational Intelligence in Emerging Technologies for Engineering … Cited by 11
• A local search with a surrogate assisted option for instance reduction
  F Neri, I Triguero
  International Conference on the Applications of Evolutionary Computation … Cited by 7
• Current trends of granular data mining for biomedical data analysis
  A Liew
  Information Sciences Cited by 7

2019 (17)

• Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study
  M Canizo, I Triguero, A Conde, E Onieva
  Neurocomputing 363, 246-260 Cited by 540
• Transforming big data into smart data: An insight on the use of the k‐nearest neighbors algorithm to obtain quality data
  I Triguero, D García‐Gil, J Maillo, J Luengo, S García, F Herrera
  Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 9 (2 … Cited by 297
• A review on the self and dual interactions between machine learning and optimisation
  H Song, I Triguero, E Özcan
  Progress in Artificial Intelligence 8 (2), 143-165 Cited by 140
• Evolving deep CNN-LSTMs for inventory time series prediction
  N Xue, I Triguero, GP Figueredo, D Landa-Silva
  2019 IEEE Congress on Evolutionary Computation (CEC), 1517-1524 Cited by 90
• Fast and Scalable Approaches to Accelerate the Fuzzy k-Nearest Neighbors Classifier for Big Data
  J Maillo, S García, J Luengo, F Herrera, I Triguero
  IEEE Transactions on Fuzzy Systems 28 (5), 874-886 Cited by 61
• A taxonomy of traffic forecasting regression problems from a supervised learning perspective
  JS Angarita-Zapata, AD Masegosa, I Triguero
  IEEE Access 7, 68185-68205 Cited by 54
• Handling uncertainty in citizen science data: Towards an improved amateur-based large-scale classification
  M Jiménez, I Triguero, R John
  Information Sciences 479, 301-320 Cited by 39
• Virtual porous materials to predict the air void topology and hydraulic conductivity of asphalt roads
  M Aboufoul, A Chiarelli, I Triguero, A Garcia
  Powder Technology 352, 294-304 Cited by 38
• Instance reduction for one-class classification
  B Krawczyk, I Triguero, S García, M Woźniak, F Herrera
  Knowledge and Information Systems 59 (3), 601-628 Cited by 36
• PAS3-HSID: A dynamic bio-inspired approach for real-time hot spot identification in data streams
  R Tickle, I Triguero, GP Figueredo, M Mesgarpour, RI John
  Cognitive Computation 11 (3), 434-458 Cited by 8
• A Simulation-based Optimisation Approach for Inventory Management of Highly Perishable Food.
  N Xue, D Landa-Silva, GP Figueredo, I Triguero
  ICORES, 406-413 Cited by 7
• IEEE access special section editorial: Data mining and granular computing in big data and knowledge processing
  W Ding, GG Yen, G Beliakov, I Triguero, M Pratama, X Zhang, H Li
  IEEE Access 7, 47682-47686 Cited by 5
• Guest editorial: computational intelligence for big data analytics
  A Fernández, I Triguero, M Galar, F Herrera
  Cognitive Computation 11 (3), 329-330 Cited by 4
• Fuzzy Hot Spot Identification for Big Data: An Initial Approach
  R Tickle, I Triguero, GP Figueredo, M Mesgarpour, RI John
  2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-6 Cited by 1
• Introduction to the Special Issue on Human-interaction-aware Data Analytics for Cyber-physical Systems
  T Wei, J Zhou, R Ranjan, I Triguero, H Yu, CJ Xue, S Dustdar
  ACM Transactions on Cyber-Physical Systems 3 (4), 1-2
• A Preliminary Approach for the Exploitation of Citizen Science Data for Fast and Robust Fuzzy k-Nearest Neighbour Classification
  M Jiménez, MT Torres, R John, I Triguero
  2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-6
• Conceptual Programming with Python
  T Altenkirch, I Triguero
  Lulu. com

2018 (10)

• On the use of convolutional neural networks for robust classification of multiple fingerprint captures
  D Peralta, I Triguero, S García, Y Saeys, JM Benitez, F Herrera
  International Journal of Intelligent Systems 33 (1), 213-230 Cited by 121
• Self-labeling techniques for semi-supervised time series classification: an empirical study
  M González, C Bergmeir, I Triguero, Y Rodríguez, JM Benítez
  Knowledge and Information Systems 55 (2), 493-528 Cited by 34
• A genetic algorithm with composite chromosome for shift assignment of part-time employees
  N Xue, D Landa-Silva, I Triguero, GP Figueredo
  2018 IEEE Congress on Evolutionary Computation (CEC), 1-8 Cited by 25
• A preliminary study on automatic algorithm selection for short-term traffic forecasting
  JS Angarita-Zapata, I Triguero, AD Masegosa
  International Symposium on Intelligent and Distributed Computing, 204-214 Cited by 18
• Coevolutionary fuzzy attribute order reduction with complete attribute-value space tree
  W Ding, I Triguero, CT Lin
  IEEE Transactions on Emerging Topics in Computational Intelligence 5 (1 … Cited by 15
• A preliminary study on hybrid spill-tree fuzzy k-nearest neighbors for big data classification
  J Maillo, J Luengo, S García, F Herrera, I Triguero
  2018 IEEE international conference on fuzzy systems (fuzz-IEEE), 1-8 Cited by 13
• A preliminary study of the feasibility of global evolutionary feature selection for big datasets under apache spark
  M Galar, I Triguero, H Bustince, F Herrera
  2018 IEEE Congress on Evolutionary Computation (CEC), 1-8 Cited by 7
• Virtual Asphalt to Predict Roads’ Air Voids and Hydraulic Conductivity
  M Aboufoul, A Chiarelli, I Triguero, A Garcia
  Preprints Cited by 3
• Un enfoque aproximado para acelerar el algoritmo de clasificacion Fuzzy kNN para Big Data
  JL Maillo, J Luengo, S García, F Herrera, I Triguero
  Asociación Española para la Inteligencia Artificial (AEPIA), 1143-1148 Cited by 3
• A first approach for handling uncertainty in citizen science
  M Jiménez
  2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-8 Cited by 2

2017 (9)

• kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data
  J Maillo, S Ramírez, I Triguero, F Herrera
  Knowledge-Based Systems 117, 3-15 Cited by 441
• KEEL 3.0: an open source software for multi-stage analysis in data mining
  I Triguero, S González, JM Moyano, S García, J Alcalá-Fdez, J Luengo et al.
  International Journal of Computational Intelligence Systems 10 (1), 1238-1249 Cited by 339
• Distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection
  D Peralta, I Triguero, S García, Y Saeys, JM Benitez, F Herrera
  Knowledge-Based Systems 126, 91-103 Cited by 44
• Exact fuzzy k-nearest neighbor classification for big datasets
  J Maillo, J Luengo, S García, F Herrera, I Triguero
  2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-6 Cited by 36
• A first attempt on global evolutionary undersampling for imbalanced big data
  I Triguero, M Galar, H Bustince, F Herrera
  2017 IEEE congress on evolutionary computation (CEC), 2054-2061 Cited by 32
• Vehicle incident hot spots identification: An approach for big data
  I Triguero, GP Figueredo, M Mesgarpour, JM Garibaldi, RI John
  2017 IEEE Trustcom/BigDataSE/ICESS, 901-908 Cited by 16
• An immune-inspired technique to identify heavy goods vehicles incident hot spots
  GP Figueredo, I Triguero, M Mesgarpour, AM Guerra, JM Garibaldi et al.
  IEEE Transactions on Emerging Topics in Computational Intelligence 1 (4 … Cited by 8
• Robust classification of different fingerprint copies with deep neural networks for database penetration rate reduction
  D Peralta, I Triguero, S García, Y Saeys, JM Benitez, F Herrera
  arXiv preprint arXiv:1703.07270 Cited by 5
• Fingerprint classification with a new deep neural network model: robustness for different captures of the same fingerprints
  D Peralta, I Triguero, S García, Y Saeys, JM Benítez, F Herrera
  CoRR abs 1703 Cited by 1

2016 (8)

• Evolutionary undersampling for extremely imbalanced big data classification under apache spark
  I Triguero, M Galar, D Merino, J Maillo, H Bustince, F Herrera
  2016 IEEE congress on evolutionary computation (CEC), 640-647 Cited by 91
• Labelling strategies for hierarchical multi-label classification techniques
  I Triguero, C Vens
  Pattern Recognition 56, 170-183 Cited by 65
• EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data
  S Vluymans, I Triguero, C Cornelis, Y Saeys
  Neurocomputing 216, 596-610 Cited by 35
• On the stopping criteria for k-nearest neighbor in positive unlabeled time series classification problems
  M Gonzalez, C Bergmeir, I Triguero, Y Rodriguez, JM Benitez
  Information Sciences 328, 42-59 Cited by 34
• DPD-DFF: A dual phase distributed scheme with double fingerprint fusion for fast and accurate identification in large databases
  D Peralta, I Triguero, S García, F Herrera, JM Benitez
  Information Fusion 32, 40-51 Cited by 31
• From big data to smart data with the k-nearest neighbours algorithm
  I Triguero, J Maillo, J Luengo, S García, F Herrera
  2016 IEEE International Conference on Internet of Things (iThings) and IEEE … Cited by 30
• Comparison of KEEL versus open source Data Mining tools: Knime and Weka software
  J Alcala-Fdez, S Garcia, A Fernandez, J Luengo, S Gonzalez, JA Saez et al. Cited by 8
• Partitioning the target space in multi-output learning
  I Triguero, M Basgalupp, R Cerri, L Schietgat, C Vens
  Proceedings of the 25th Belgian-Dutch Machine Learning Conference (Benelearn) Cited by 1

2015 (12)

• Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study
  I Triguero, S García, F Herrera
  Knowledge and Information Systems 42, 245-284 Cited by 825
• MRPR: A MapReduce solution for prototype reduction in big data classification
  I Triguero, D Peralta, J Bacardit, S García, F Herrera
  neurocomputing 150, 331-345 Cited by 272
• A survey on fingerprint minutiae-based local matching for verification and identification: Taxonomy and experimental evaluation
  D Peralta, M Galar, I Triguero, D Paternain, S García, E Barrenechea et al.
  Information Sciences 315, 67-87 Cited by 204
• ROSEFW-RF: the winner algorithm for the ECBDL’14 big data competition: an extremely imbalanced big data bioinformatics problem
  I Triguero, S Del Río, V López, J Bacardit, JM Benítez, F Herrera
  Knowledge-Based Systems 87, 69-79 Cited by 199
• Evolutionary feature selection for big data classification: A mapreduce approach
  D Peralta, S Del Río, S Ramírez-Gallego, I Triguero, JM Benitez et al.
  Mathematical Problems in Engineering 2015 (1), 246139 Cited by 192
• A mapreduce-based k-nearest neighbor approach for big data classification
  J Maillo, I Triguero, F Herrera
  2015 IEEE Trustcom/BigDataSE/ISPA 2, 167-172 Cited by 115
• A survey of fingerprint classification Part I: Taxonomies on feature extraction methods and learning models
  M Galar, J Derrac, D Peralta, I Triguero, D Paternain, C Lopez-Molina et al.
  Knowledge-based systems 81, 76-97 Cited by 101
• Evolutionary undersampling for imbalanced big data classification
  I Triguero, M Galar, S Vluymans, C Cornelis, H Bustince, F Herrera et al.
  2015 IEEE Congress on Evolutionary Computation (CEC), 715-722 Cited by 88
• SEG-SSC: A Framework Based on Synthetic Examples Generation for Self-Labeled Semi-Supervised Classification
  I Triguero, S García
  IEEE Transactions on Cybernetics 45 (4), 622-634 Cited by 88
• A survey of fingerprint classification part II: experimental analysis and ensemble proposal
  M Galar, J Derrac, D Peralta, I Triguero, D Paternain, C Lopez-Molina et al.
  Knowledge-Based Systems 81, 98-116 Cited by 63
• Representational power of gene features for function prediction
  K Pliakos, I Triguero, D Kocev, C Vens
  Benelux Bioinformatics Conference 2015 (BBC 2015), Date: 2015/12/07-2015/12 … Cited by 4
• Supplementary material for “DPD-DFF: A Dual Phase Distributed Scheme with Double Fingerprint Fusion for Fast and Accurate Identification in Large Databases”
  D Peralta, I Triguero, S García, F Herrera, JM Benitez
  Soft Computing and Intelligent Information Systems, University of Granada Cited by 1

2014 (8)

• On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification
  I Triguero, JA Sáez, J Luengo, S García, F Herrera
  Neurocomputing 132, 30-41 Cited by 114
• Fast fingerprint identification for large databases
  D Peralta, I Triguero, R Sanchez-Reillo, F Herrera, JM Benítez
  Pattern recognition 47 (2), 588-602 Cited by 100
• Addressing imbalanced classification with instance generation techniques: IPADE-ID
  V López, I Triguero, CJ Carmona, S García, F Herrera
  Neurocomputing 126, 15-28 Cited by 68
• Minutiae filtering to improve both efficacy and efficiency of fingerprint matching algorithms
  D Peralta, M Galar, I Triguero, O Miguel-Hurtado, JM Benitez, F Herrera
  Engineering Applications of Artificial Intelligence 32, 37-53 Cited by 52
• Multi-objective evolutionary algorithms for the design of grid-connected solar tracking systems
  D Gómez-Lorente, I Triguero, C Gil, O Rabaza
  International Journal of Electrical Power & Energy Systems 61, 371-379 Cited by 17
• A combined mapreduce-windowing two-level parallel scheme for evolutionary prototype generation
  I Triguero, D Peralta, J Bacardit, S García, F Herrera
  2014 IEEE Congress on Evolutionary Computation (CEC), 3036-3043 Cited by 14
• A first attempt on evolutionary prototype reduction for nearest neighbor one-class classification
  B Krawczyk, I Triguero, S García, M Woźniak, F Herrera
  2014 IEEE Congress on Evolutionary Computation (CEC), 747-753 Cited by 5
• Improving disease prediction using unlabeled and synthetic samples
  I Triguero, S García, F Herrera, Y Saeys
  Proceedings of the Benelux Bioinformatics Conference, 72 Cited by 1

2012 (8)

• Evolutionary-based selection of generalized instances for imbalanced classification
  S Garcí, I Triguero, CJ Carmona, F Herrera
  Knowledge-Based Systems 25 (1), 3-12 Cited by 177
• Integrating instance selection, instance weighting, and feature weighting for nearest neighbor classifiers by coevolutionary algorithms
  J Derrac, I Triguero, S García, F Herrera
  IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42 … Cited by 74
• Evolutionary algorithms for the design of grid-connected PV-systems
  D Gómez-Lorente, I Triguero, C Gil, AE Estrella
  Expert Systems with Applications 39 (9), 8086-8094 Cited by 46
• Integrating a differential evolution feature weighting scheme into prototype generation
  I Triguero, J Derrac, S Garcia, F Herrera
  Neurocomputing 97, 332-343 Cited by 29
• Time series modeling and forecasting using memetic algorithms for regime-switching models
  C Bergmeir, I Triguero, D Molina, JL Aznarte, JM Benitez
  IEEE transactions on neural networks and learning systems 23 (11), 1841-1847 Cited by 25
• A co-evolutionary framework for nearest neighbor enhancement: Combining instance and feature weighting with instance selection
  J Derrac, I Triguero, S García, F Herrera
  International Conference on Hybrid Artificial Intelligence Systems, 176-187 Cited by 4
• Algoritmos Basados en Nubes de Partículas y Evolución Diferencial para el Problema de Optimización Continua: Un estudio experimental
  PD Gutiérrez, I Triguero, F Herrera
  Actas del VIII Congreso Español sobre Metaheurística, Algoritmos Evolutivos … Cited by 1
• Optimization of neuro-coefficient smooth transition autoregressive models using differential evolution
  C Bergmeir, I Triguero, F Velasco, JM Benítez
  International Conference on Hybrid Artificial Intelligence Systems, 464-473

2011 (6)

• A taxonomy and experimental study on prototype generation for nearest neighbor classification
  I Triguero, J Derrac, S Garcia, F Herrera
  IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and … Cited by 347
• Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification
  I Triguero, S García, F Herrera
  Pattern recognition 44 (4), 901-916 Cited by 147
• A study of the scaling up capabilities of stratified prototype generation
  I Triguero, J Derrac, F Herrera, S García
  2011 Third World Congress on Nature and Biologically Inspired Computing, 297-302 Cited by 6
• Enhancing IPADE algorithm with a different individual codification
  I Triguero, S García, F Herrera
  International Conference on Hybrid Artificial Intelligence Systems, 262-270 Cited by 5
• Prototype generation for nearest neighbor classification: Survey of methods
  I Triguero, J Derrac, S García, F Herrera
  Technical Report, Department of Computer Science and Artificial Intelligence Cited by 5
• Survey of new approaches on prototype selection and generation
  J Derrac, I Triguero, S García, F Herrera
  Tech. rep., Technical Report, Department of Computer Science and Artificial … Cited by 2

2010 (3)

• IPADE: Iterative prototype adjustment for nearest neighbor classification
  I Triguero, S García, F Herrera
  IEEE Transactions on Neural Networks 21 (12), 1984-1990 Cited by 55
• A preliminary study on the use of differential evolution for adjusting the position of examples in nearest neighbor classification
  I Triguero, S García, F Herrera
  IEEE Congress on Evolutionary Computation, 1-8 Cited by 10
• A preliminary study on the selection of generalized instances for imbalanced classification
  S García, J Derrac, I Triguero, C Carmona, F Herrera
  International Conference on Industrial, Engineering and Other Applications …

Other (6)

• Un enfoque MapReduce del algoritmo k-vecinos más cercanos para Big Data
  J Maillo, I Triguero, F Herrera
  ACM 7, 971-980 Cited by 8
• Evolución Diferencial para Reducción de Prototipos y Ponderación de Características
  I Triguero, J Derrac, S García, F Herrera Cited by 1
• Exploring TabPFNv2 as a Novel Baseline for ADMET Prediction in Drug Discovery
  O Ipas, IS Martín, G Gomez-Trenado, I Triguero, R Romero-Zaliz
• Computación Evolutiva para el Diseño y Mejora de Sistemas Inteligentes de Propósito General: Estudio y perspectivas
  D Molina, JP Amador, J Del Ser, S Garcia, H Ishibuchi, I Triguero, B Xue et al.
  XVI Congreso Español de Metaheurísticas, Algoritmos Evolutivos …
• EMERGENT TOPICS IN ARTIFICIAL IMMUNE SYSTEMS
  GC Silva, WM Caminhas, L de Errico, GP Figueredo, I Triguero et al.
• Un esquema de pesos basado en evolución diferencial para generación de prototipos
  I Triguero, J Derrac, S García, F Herrera