Isaac Triguero Velázquez
Contact
triguero@decs6nwTPUvOai.ugr.es
Organization
UGR| Total | From 2019: | |
|---|---|---|
| Citas | Total: 5896 | From 2019: 4133 |
| Índice H | Total: 35 | From 2019: 31 |
| Índice i10 | Total: 64 | From 2019: 59 |
Papers (144)
| Title | Authors | Year |
|---|---|---|
| SEGAL time series classification—Stable explanations using a generative model and an adaptive weighting method for LIME | H Meng, C Wagner, I Triguero. | 2024 |
| Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects | J Poyatos, J Del Ser, S Garcia, H Ishibuchi, D Molina, I Triguero, B Xue, .... | 2024 |
| 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, .... | 2024 |
| 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, .... | 2024 |
| 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 |
| A Preliminary Study on Preprocessing the Semantic Space in Zero-Shot Learning | JJH Aranda, F Herrera, I Triguero. | 2024 |
| Machine Learning Pipeline for Energy and Environmental Prediction in Cold Storage Facilities | N Alkhulaifi, AL Bowler, D Pekaslanc, G Serdaroglu, S Closs, NJ Watson, .... | 2024 |
| CzSL: A new learning paradigm for astronomical image classification with citizen science | M Jimenez, EJ Alfaro, MT Torres, I Triguero. | 2023 |
| Forecasting Solar Irradiance without Direct Observation: An Empirical Analysis | T Cargan, D Landa-Silva, I Triguero. | 2023 |
| Identifying bird species by their calls in Soundscapes | K Maclean, I Triguero. | 2023 |
| AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting | JS Angarita-Zapata, AD Masegosa, I Triguero. | 2023 |
| Large-scale Data Analytics with Python and Spark: A Hands-on Guide to Implementing Machine Learning Solutions | I Triguero, M Galar. | 2023 |
| Explaining Time Series Classifiers through Meaningful Perturbation and Optimisation | H Meng, C Wagner, I Triguero. | 2023 |
| General Purpose Artificial Intelligence Systems (GPAIS): Properties, Definition, Taxonomy, Open Challenges and Implications | I Triguero, D Molina, J Poyatos, J Del Ser, F Herrera. | 2023 |
| Hyper-Stacked: Scalable and Distributed Approach to AutoML for Big Data | R Dave, JS Angarita-Zapata, I Triguero. | 2023 |
| CzSL: Learning from citizen science, experts and unlabelled data in astronomical image classification | M Jiménez, EJ Alfaro, MT Torres, I Triguero. | 2023 |
| CzSL: Learning from citizen science, experts, and unlabelled data in astronomical image classification | M Jiménez, EJ Alfaro, M Torres Torres, I Triguero. | 2023 |
| 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. | 2023 |
| The Energy Prediction Smart-Meter Dataset: Analysis of Previous Competitions and Beyond | D Pekaslan, JM Alonso-Moral, K Bandara, C Bergmeir, .... | 2023 |
| An Initial Step Towards Stable Explanations for Multivariate Time Series Classifiers with LIME | H Meng, C Wagner, I Triguero. | 2023 |
| Local-Global Methods for Generalised Solar Irradiance Forecasting | TR Cargan, D Landa-Silva, I Triguero. | 2023 |
| Forecasting Solar Irradiance without Direct Observation: An Empirical Analysis. | T Cargan, D Landa-Silva, I Triguero. | 2023 |
| SPMS-ALS: A Single-Point Memetic structure with accelerated local search for instance reduction | HL Le, F Neri, I Triguero. | 2022 |
| A fusion spatial attention approach for few-shot learning | H Song, B Deng, M Pound, E Özcan, I Triguero. | 2022 |
| Accelerated pattern search with variable solution size for simultaneous instance selection and generation | HL Le, F Neri, D Landa-Silva, I Triguero. | 2022 |
| 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, .... | 2022 |
| Forced vital capacity trajectories in patients with idiopathic pulmonary fibrosis: a secondary analysis of a multicentre, prospective, observational cohort | HP Fainberg, JM Oldham, PL Molyneau, RJ Allen, LM Kraven, WA Fahy, .... | 2022 |
| Feature Importance Identification for Time Series Classifiers | H Meng, C Wagner, I Triguero. | 2022 |
| Comparison and Evaluation of Methods for a Predict+ Optimize Problem in Renewable Energy | C Bergmeir, F de Nijs, A Sriramulu, M Abolghasemi, R Bean, J Betts, .... | 2022 |
| Citizen Science and Machine Learning: Towards a Robust Large-Scale Automatic Classification in Astronomy | M Jiménez, EJ Alfaro, I Triguero. | 2022 |
| EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification | HL Le, D Landa-Silva, M Galar, S Garcia, I Triguero. | 2021 |
| Beyond global and local multi-target learning | M Basgalupp, R Cerri, L Schietgat, I Triguero, C Vens. | 2021 |
| Few-Shot Learning for Postnatal Gestational Age Estimation | S Romanov, H Song, M Valstar, D Sharkey, C Henry, I Triguero, .... | 2021 |
| Current trends of granular data mining for biomedical data analysis | W Ding, CT Lin, AWC Liew, I Triguero, W Luo. | 2020 |
| A Local Search with a Surrogate Assisted Option for Instance Reduction | F Neri, I Triguero. | 2020 |
| Multigranulation Super-Trust Model for Attribute Reduction | W Ding, W Pedrycz, I Triguero, Z Cao, CT Lin. | 2020 |
| Galaxy Image Classification Based on Citizen Science Data: A Comparative Study | M Jiménez, MT Torres, R John, I Triguero. | 2020 |
| Redundancy and Complexity Metrics for Big Data Classification: Towards Smart Data | J Maillo, I Triguero, F Herrera. | 2020 |
| General-Purpose Automated Machine Learning for Transportation: A Case Study of Auto-sklearn for Traffic Forecasting | JS Angarita-Zapata, AD Masegosa, I Triguero. | 2020 |
| 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. | 2020 |
| Decomposition-Fusion for Label Distribution Learning | M González, G González-Almagro, I Triguero, JR Cano, S García. | 2020 |
| A Hybrid Surrogate Model for Evolutionary Undersampling in Imbalanced Classification | HL Le, D Landa-Silva, M Galar, S Garcia, I Triguero. | 2020 |
| 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 |
| Multigranulation supertrust model for attribute reduction | W Ding, W Pedrycz, I Triguero, Z Cao, CT Lin. | 2020 |
| FUZZ-IEEE Competition on Explainable Energy Prediction | I Triguero, JM Alonso, L Magdalena, C Wagner, J BernabÃl’-Moreno. | 2020 |
| 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. | 2019 |
| Instance reduction for one-class classification | B Krawczyk, I Triguero, S García, M Woźniak, F Herrera. | 2019 |
| Fast and Scalable Approaches to Accelerate the Fuzzy k Nearest Neighbors Classifier for Big Data | J Maillo, I Triguero, J Luengo, FH S. García. | 2019 |
| Handling uncertainty in citizen science data: Towards an improved amateur-based large-scale classification | M Jiménez, I Triguero, R John. | 2019 |
| Virtual porous materials to predict the air void topology and hydraulic conductivity of asphalt roads | M Aboufoul, A Chiarelli, I Triguero, A Garcia. | 2019 |
| 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. | 2019 |
| A Simulation-Based Optimisation Approach for Inventory Management of Highly Perishable Food | N Xue, D Landa-Silva, GP Figueredo, I Triguero. | 2019 |
| Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study | M Canizo, I Triguero, A Conde, E Onieva. | 2019 |
| 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. | 2019 |
| Evolving Deep CNN-LSTMs for Inventory Time Series Prediction | N Xue, I Triguero, GP Figueredo, D Landa-Silva. | 2019 |
| Evaluating automated machine learning on supervised regression traffic forecasting problems | JS Angarita-Zapata, AD Masegosa, I Triguero. | 2019 |
| A review on the self and dual interactions between machine learning and optimisation | H Song, I Triguero, E Özcan. | 2019 |
| A Taxonomy of Traffic Forecasting Regression Problems from a Supervised Learning Perspective | JS Angarita-Zapata, AD Masegosa, I Triguero. | 2019 |
| 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. | 2019 |
| L2AE-D: Learning to Aggregate Embeddings for Few-shot Learning with Meta-level Dropout | H Song, MT Torres, E Özcan, I Triguero. | 2019 |
| Guest Editorial: Computational Intelligence for Big Data Analytics | A Fernandez, I Triguero, M Galar, F Herrera. | 2019 |
| 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 |
| Fuzzy Hot Spot Identification for Big Data: An Initial Approach | R Tickle, I Triguero, GP Figueredo, M Mesgarpour, RI John. | 2019 |
| 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. | 2019 |
| A Simulation-based Optimisation Approach for Inventory Management of Highly Perishable Food. | N Xue, D Landa-Silva, GP Figueredo, I Triguero. | 2019 |
| Conceptual Programming with Python | T Altenkirch, I Triguero. | 2019 |
| ssc: An R Package for Semi-Supervised Classification | M González, O Rosado, JD Rodríguez, C Bergmeir, I Triguero, JM Benítez. | 2019 |
| 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 |
| 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. | 2018 |
| Self-labeling techniques for semi-supervised time series classification: an empirical study | M González, C Bergmeir, I Triguero, Y Rodríguez, JM Benítez. | 2018 |
| 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 |
| Un enfoque aproximado para acelerar el algoritmo de clasificacion Fuzzy kNN para Big Data | J Maillo, J Luengo, S Garcıa, F Herrera, I Triguero. | 2018 |
| Virtual Asphalt to Predict Roads’ Air Voids and Hydraulic Conductivity | M Aboufoul, A Chiarelli, I Triguero, A Garcia. | 2018 |
| A genetic algorithm with composite chromosome for shift assignment of part-time employees | N Xue, D Landa-Silva, I Triguero, GP Figueredo. | 2018 |
| A preliminary study on automatic algorithm selection for short-term traffic forecasting | JS Angarita-Zapata, I Triguero, AD Masegosa. | 2018 |
| A first approach for handling uncertainty in citizen science | M Jiménez. | 2018 |
| Coevolutionary Fuzzy Attribute Order Reduction With Complete Attribute-Value Space Tree | W Ding, I Triguero, CT Lin. | 2018 |
| KEEL 3.0: an open source software for multi-stage analysis in data mining | I Triguero, S González, JM Moyano, S García López, J Alcalá Fernández, .... | 2017 |
| Exact fuzzy k-nearest neighbor classification for big datasets | J Maillo, J Luengo, S García, F Herrera, I Triguero. | 2017 |
| 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. | 2017 |
| 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. | 2017 |
| kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data | J Maillo, S Ramírez, I Triguero, F Herrera. | 2017 |
| A first attempt on global evolutionary undersampling for imbalanced big data | I Triguero, M Galar, H Bustince, F Herrera. | 2017 |
| Vehicle incident hot spots identification: An approach for big data | I Triguero, GP Figueredo, M Mesgarpour, JM Garibaldi, RI John. | 2017 |
| An immune-inspired technique to identify heavy goods vehicles incident hot spots | GP Figueredo, I Triguero, M Mesgarpour, AM Guerra, JM Garibaldi, .... | 2017 |
| 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. | 2017 |
| 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. | 2017 |
| From big data to smart data with the k-nearest neighbours algorithm | I Triguero, J Maillo, J Luengo, S García, F Herrera. | 2016 |
| Comparison of KEEL versus open source Data Mining tools: Knime and Weka software | I Triguero, S González, JM Moyano, S García, J Alcala-Fdez, J Luengo, .... | 2016 |
| On the stopping criteria for k-Nearest Neighbor in positive unlabeled time series classification problems | M González, C Bergmeir, I Triguero, Y Rodríguez, JM Benítez. | 2016 |
| 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. | 2016 |
| Evolutionary undersampling for extremely imbalanced big data classification under apache spark | I Triguero, M Galar, D Merino, J Maillo, H Bustince, F Herrera. | 2016 |
| EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data | S Vluymans, I Triguero, C Cornelis, Y Saeys. | 2016 |
| Labelling strategies for hierarchical multi-label classification techniques | I Triguero, C Vens. | 2016 |
| and Herrera, Francisco (2016) kNN-IS: an iterative spark-based design of the k-nearest neighbors classifier for big data. Knowledge-Based Systems. ISSN 1872 | J Maillo, S Ramirez, I Triguero. | 2016 |
| Partitioning the target space in multi-output learning | I Triguero, M Basgalupp, R Cerri, L Schietgat, C Vens. | 2016 |
| 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, .... | 2015 |
| 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. | 2015 |
| Evolutionary feature selection for big data classification: A mapreduce approach | D Peralta, S del Río, S Ramírez-Gallego, I Triguero, JM Benitez, F Herrera. | 2015 |
| 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, .... | 2015 |
| 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, .... | 2015 |
| 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. | 2015 |
| Research Article Evolutionary Feature Selection for Big Data Classification: A MapReduce Approach | D Peralta, S del Río, S Ramírez-Gallego, I Triguero, JM Benitez, F Herrera. | 2015 |
| Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study | I Triguero, S García, F Herrera. | 2015 |
| MRPR: A MapReduce solution for prototype reduction in big data classification | I Triguero, D Peralta, J Bacardit, S García, F Herrera. | 2015 |
| SEG-SSC: A Framework Based on Synthetic Examples Generation for Self-Labeled Semi-Supervised Classification | I Triguero, S García, F Herrera. | 2015 |
| A mapreduce-based k-nearest neighbor approach for big data classification | J Maillo, I Triguero, F Herrera. | 2015 |
| Evolutionary undersampling for imbalanced big data classification | I Triguero, M Galar, S Vluymans, C Cornelis, H Bustince, F Herrera, .... | 2015 |
| Un enfoque MapReduce del algoritmo k-vecinos más cercanos para Big Data | J Maillo, I Triguero, F Herrera. | 2015 |
| Representational power of gene features for function prediction | K Pliakos, I Triguero, D Kocev, C Vens. | 2015 |
| A survey on fingerprint minutiae-based local matching for | D Peralta, M Galar, I Triguero, D Paternain, S García, E Barrenechea, .... | 2015 |
| EdurneBarrenechea, José M. Benítez, Humberto Bustince, Francisco Herrera,―A survey on fingerprint minutiae-based local matching for verification and identification: Taxonomy … | D Peralta, M Galar, I Triguero, D Paternain, S Garcíaa. | 2015 |
| Addressing imbalanced classification with instance generation techniques: IPADE-ID | V López, I Triguero, CJ Carmona, S García, F Herrera. | 2014 |
| 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. | 2014 |
| Fast fingerprint identification for large databases | D Peralta, I Triguero, R Sanchez-Reillo, F Herrera, JM Benítez. | 2014 |
| 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. | 2014 |
| A combined mapreduce-windowing two-level parallel scheme for evolutionary prototype generation | I Triguero, D Peralta, J Bacardit, S García, F Herrera. | 2014 |
| A first attempt on evolutionary prototype reduction for nearest neighbor one-class classification | B Krawczyk, I Triguero, S García, M Woźniak, F Herrera. | 2014 |
| Improving Disease Prediction using Unlabeled and Synthetic Samples | I Triguero, S García, F Herrera, Y Saeys. | 2014 |
| Multi-objective evolutionary algorithms for the design of grid-connected solar tracking systems | D Gómez-Lorente, I Triguero, C Gil, O Rabaza. | 2014 |
| Posiciones de las universidades españolas y de las comunidades autónomas en los Rankings I-UGR según campos y disciplinas científicas | D Torres-Salinas, E Delgado López-Cózar, N Robinson-García, I Triguero, .... | 2013 |
| Evolutionary-based selection of generalized instances for imbalanced classification | S Garcı, I Triguero, CJ Carmona, F Herrera. | 2012 |
| Time series modeling and forecasting using memetic algorithms for regime-switching models | C Bergmeir, I Triguero, D Molina, JL Aznarte, JM Benitez. | 2012 |
| Optimization of neuro-coefficient smooth transition autoregressive models using differential evolution | C Bergmeir, I Triguero, F Velasco, JM Benítez. | 2012 |
| A taxonomy and experimental study on prototype generation for nearest neighbor classification | I Triguero, J Derrac, S Garcia, F Herrera. | 2012 |
| Integrating instance selection, instance weighting, and feature weighting for nearest neighbor classifiers by coevolutionary algorithms | J Derrac, I Triguero, S García, F Herrera. | 2012 |
| Integrating a differential evolution feature weighting scheme into prototype generation | I Triguero, JN Derrac, S GarcíA, F Herrera. | 2012 |
| A co-evolutionary framework for nearest neighbor enhancement: Combining instance and feature weighting with instance selection | J Derrac, I Triguero, S García, F Herrera. | 2012 |
| Algoritmos Basados en Nubes de Partıculas y Evolución Diferencial para el Problema de Optimización Continua: Un estudio experimental | PD Gutiérrez, I Triguero, F Herrera. | 2012 |
| Evolutionary algorithms for the design of grid-connected PV-systems | D Gómez-Lorente, I Triguero, C Gil, AE Estrella. | 2012 |
| Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification | I Triguero, S García, F Herrera. | 2011 |
| A study of the scaling up capabilities of stratified prototype generation | I Triguero, J Derrac, F Herrera, S García. | 2011 |
| Enhancing IPADE algorithm with a different individual codification | I Triguero, S García, F Herrera. | 2011 |
| Prototype Generation for Nearest Neighbor Classification: Survey of Methods | I Triguero, J Derrac, S Garcıa, F Herrera. | 2011 |
| Survey of New Approaches on Prototype Selection and Generation | J Derrac, I Triguero, S Garcıa, F Herrera. | 2011 |
| A preliminary study on the selection of generalized instances for imbalanced classification | S García, J Derrac, I Triguero, C Carmona, F Herrera. | 2010 |
| IPADE: Iterative prototype adjustment for nearest neighbor classification | I Triguero, S García, F Herrera. | 2010 |
| 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. | 2010 |
Descargando datos de la publicación