Salvador García López
Contact
salvagl@de@PVFCf75uuHmcsai.ugr.es
Organization
UGRHighly Cited Researcher 2014, 2015, 2016, 2017, 2018, 2019 and 2020 Ver CV
| Total | From 2019: | |
|---|---|---|
| Citas | Total: 47229 | From 2019: 33649 |
| Índice H | Total: 65 | From 2019: 55 |
| Índice i10 | Total: 145 | From 2019: 125 |
Papers (250)
| Title | Authors | Year |
|---|---|---|
| Hybrid Gromov–Wasserstein Embedding for Capsule Learning | P Shamsolmoali, M Zareapoor, S Das, E Granger, S García. | 2024 |
| Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023) | M Salvi, MR Acharya, S Seoni, O Faust, RS Tan, PD Barua, S García, .... | 2024 |
| Robust multi-modal pedestrian detection using deep convolutional neural network with ensemble learning model | DK Jain, S Garcia, S Neelakandan. | 2024 |
| Application of spatial uncertainty predictor in CNN-BiLSTM model using coronary artery disease ECG signals | S Seoni, F Molinari, UR Acharya, OS Lih, PD Barua, S García, M Salvi. | 2024 |
| RSAFormer: A method of polyp segmentation with region self-attention transformer | X Yin, J Zeng, T Hou, C Tang, C Gan, DK Jain, S García. | 2024 |
| Metric learning for monotonic classification: turning the space up to the limits of monotonicity | JL Suárez, G González-Almagro, S García, F Herrera. | 2024 |
| Video multimodal sentiment analysis using cross-modal feature translation and dynamical propagation | C Gan, Y Tang, X Fu, Q Zhu, DK Jain, S García. | 2024 |
| Predict. Optimize. Revise. On Forecast and Policy Stability in Energy Management Systems | E Genov, J Ruddick, C Bergmeir, M Vafaeipour, T Coosemans, S Garcia, .... | 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 |
| Fractional Correspondence Framework in Detection Transformer | M Zareapoor, P Shamsolmoali, H Zhou, Y Lu, S Garcia. | 2024 |
| UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data | B Ahadzadeh, M Abdar, M Foroumandi, F Safara, A Khosravi, S García, .... | 2024 |
| Improved binary differential evolution with dimensionality reduction mechanism and binary stochastic search for feature selectionm | S Mirjalili, B Ahadzadeh, M Abdar, F Safara, L Aghaei, A Khosravi, .... | 2024 |
| Semi-Supervised Constrained Clustering: An In-Depth Overview, Ranked Taxonomy and Future Research Directions | G González-Almagro, D Peralta, E De Poorter, JR Cano, S García. | 2023 |
| Semi-supervised Clustering with Two Types of Background Knowledge: Fusing Pairwise Constraints and Monotonicity Constraints | G González-Almagro, JL Suárez, P Sánchez-Bermejo, JR Cano, S García. | 2023 |
| Binarized multi-gate mixture of Bayesian experts for cardiac syndrome X Diagnosis: A clinician-in-the-loop scenario with a belief-uncertainty fusion paradigm | M Abdar, A Mehrzadi, M Goudarzi, F Masoudkabir, L Rundo, M Mamouei, .... | 2023 |
| Correction to: An empirical study on the joint impact of feature selection and data resampling on imbalance classification | C Zhang, P Soda, J Bi, G Fan, G Almpanidis, S García, W Ding. | 2023 |
| Speech emotion recognition via multiple fusion under spatial–temporal parallel network | C Gan, K Wang, Q Zhu, Y Xiang, DK Jain, S García. | 2023 |
| Clustering approximation via a fusion of multiple random samples | MS Mahmud, JZ Huang, S García, H Zheng. | 2023 |
| EpilepsyNet: Novel automated detection of epilepsy using transformer model with EEG signals from 121 patient population | OS Lih, V Jahmunah, EE Palmer, PD Barua, S Dogan, T Tuncer, S García, .... | 2023 |
| On Forecast Stability | R Godahewa, C Bergmeir, ZE Baz, C Zhu, Z Song, S García, D Benavides. | 2023 |
| Multi-Modality Approaches for Medical Support Systems: A Systematic Review of the Last Decade | M Salvi, HW Loh, S Seoni, PD Barua, S García, F Molinari, UR Acharya. | 2023 |
| Enhancing microblog sentiment analysis through multi-level feature interaction fusion with social relationship guidance | C Gan, X Cao, Q Zhu, DK Jain, S García. | 2023 |
| Improved Binary Differential Evolution with Dimensionality Reduction Mechanism and Binary Stochastic Search for Feature Selection | B Ahadzadeh, M Abdar, F Safara, L Aghaei, S Mirjalili, A Khosravi, .... | 2023 |
| Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization | A Rosales-Pérez, S García, F Herrera. | 2022 |
| GEN: Generative Equivariant Networks for Diverse Image-to-Image Translation | P Shamsolmoali, M Zareapoor, S Das, S García, E Granger, J Yang. | 2022 |
| Wasserstein Embedding for Capsule Learning | P Shamsolmoali, M Zareapoor, S Das, E Granger, S Garcia. | 2022 |
| Monotonic Constrained Clustering: A First Approach | G González-Almagro, PS Bermejo, JL Suarez, JR Cano, S García. | 2022 |
| A Preliminary Approach for using Metric Learning in Monotonic Classification | JL Suárez, G González-Almagro, S García, F Herrera. | 2022 |
| Adapting-Means Algorithm for Pair-Wise Constrained Clustering of Imbalanced Data Streams | S Wojciechowski, G González-Almagro, S García, M Woźniak. | 2022 |
| Adapting K-Means Algorithm for Pair-Wise Constrained Clustering of Imbalanced Data Streams | S Wojciechowski, G González-Almagro, S García, M Woźniak. | 2022 |
| Distance Metric Learning with Prototype Selection for Imbalanced Classification | JL Suárez, S García, F Herrera. | 2021 |
| Ordinal regression with explainable distance metric learning based on ordered sequences | JL Suárez, S García, F Herrera. | 2021 |
| SOUL: Scala Oversampling and Undersampling Library for imbalance classification | N Rodríguez, D López, A Fernández, S García, F Herrera. | 2021 |
| An indexing algorithm based on clustering of minutia cylinder codes for fast latent fingerprint identification | I Pérez-Sánchez, B Cervantes, MA Medina-Pérez, R Monroy, .... | 2021 |
| How to design the fair experimental classifier evaluation | K Stapor, P Ksieniewicz, S García, M Woźniak. | 2021 |
| Enhancing instance-level constrained clustering through differential evolution | G González-Almagro, J Luengo, JR Cano, S García. | 2021 |
| 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 |
| ME-MEOA/DCC: Multiobjective constrained clustering through decomposition-based memetic elitism | G González-Almagro, A Rosales-Pérez, J Luengo, JR Cano, S García. | 2021 |
| An Empirical Study on the Joint Impact of Feature Selection and Data Resampling on Imbalance Classification | C Zhang, P Soda, J Bi, G Fan, G Almpanidis, S Garcia. | 2021 |
| Advances in domain adaptation for computer vision | P Shamsolmoali, S García, H Zhou, ME Celebi. | 2021 |
| An Empirical Study on the Joint Impact of Feature Selection and Data Re-sampling on Imbalance Classification | C Zhang, P Soda, J Bi, G Fan, G Almpanidis, S Garcia. | 2021 |
| 3SHACC: Three Stages Hybrid Agglomerative Constrained Clustering | G González-Almagro, JL Suarez, J Luengo, JR Cano, S García. | 2021 |
| Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, DaSCI, University of Granada, 18071 … | S García. | 2021 |
| Advances in domain adaptation for computer vision. | P Shamsolmoali, S García, H Zhou, ME Celebi. | 2021 |
| Similarity-based and Iterative Label Noise Filters for Monotonic Classification | JR Cano, J Luengo, S García. | 2020 |
| Smart Data based Ensemble for Imbalanced Big Data Classification | D García-Gil, J Holmberg, S García, N Xiong, F Herrera. | 2020 |
| Recent Trends in the Use of Statistical Tests for Comparing Swarm and Evolutionary Computing Algorithms: Practical Guidelines and a Critical Review | J Castillo, S García, M del Mar Rueda, S Das, F Herrera. | 2020 |
| Comprehensive Taxonomies of Nature-and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations | D Molina, J Poyatos, J Del Ser, S García, A Hussain, F Herrera. | 2020 |
| Fuzzy k-Nearest Neighbors with monotonicity constraints: Moving towards the robustness of monotonic noise | S González, S García, ST Li, R John, F Herrera. | 2020 |
| Big Data: Technologies and Tools | J Luengo, D García-Gil, S Ramírez-Gallego, S García, F Herrera. | 2020 |
| Smart Data | J Luengo, D García-Gil, S Ramírez-Gallego, S García, F Herrera. | 2020 |
| Final Thoughts: From Big Data to Smart Data | J Luengo, D García-Gil, S Ramírez-Gallego, S García, F Herrera. | 2020 |
| Big Data Discretization | J Luengo, D García-Gil, S Ramírez-Gallego, S García, F Herrera. | 2020 |
| Imbalanced Data Preprocessing for Big Data | J Luengo, D García-Gil, S Ramírez-Gallego, S García, F Herrera. | 2020 |
| Big Data Software | J Luengo, D García-Gil, S Ramírez-Gallego, S García, F Herrera. | 2020 |
| Dimensionality Reduction for Big Data | J Luengo, D García-Gil, S Ramírez-Gallego, S García, F Herrera. | 2020 |
| Data Reduction for Big Data | J Luengo, D García-Gil, S Ramírez-Gallego, S García, F Herrera. | 2020 |
| Big Data Preprocessing: Enabling Smart Data | J Luengo. | 2020 |
| Imperfect Big Data | J Luengo, D García-Gil, S Ramírez-Gallego, S García, F Herrera. | 2020 |
| DILS: constrained clustering through Dual Iterative Local Search | G González-Almagro, J Luengo, JR Cano, S García. | 2020 |
| ProLSFEO-LDL: Prototype Selection and Label-Specific Feature Evolutionary Optimization for Label Distribution Learning | M González, JR Cano, S García. | 2020 |
| pyDML: A Python Library for Distance Metric Learning | JL Suárez, S García, F Herrera. | 2020 |
| Improving constrained clustering via decomposition-based multiobjective optimization with memetic elitism | G González-Almagro, A Rosales-Pérez, J Luengo, JR Cano, S García. | 2020 |
| A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities | S González, S García, J Del Ser, L Rokach, F Herrera. | 2020 |
| Synthetic Sample Generation for Label Distribution Learning | M González, J Luengo, JR Cano, S García. | 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 |
| A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms, Experimental Analysis, Prospects and Challenges | JL Suárez, S García, F Herrera. | 2020 |
| Comprehensive taxonomies of nature-and bio-inspired optimization: Inspiration versus algorithmic behavior, critical analysis recommendations | D Molina, J Poyatos, J Del Ser, S García, A Hussain, F Herrera. | 2020 |
| pyDML: A Python Library for Distance Metric Learning. | JL Suárez, S García, F Herrera. | 2020 |
| Agglomerative Constrained Clustering Through Similarity and Distance Recalculation | G González-Almagro, JL Suarez, J Luengo, JR Cano, S García. | 2020 |
| Smart Data driven Decision Trees Ensemble Methodology for Imbalanced Big Data | D García-Gil, S García, N Xiong, F Herrera. | 2020 |
| Similarity-based and Iterative Label Noise Filters for Monotonic Classification. | JR Cano, J Luengo, S García. | 2020 |
| Smartdata: data preprocessing to achieve smart data in R | I Cordón, J Luengo, S García, F Herrera, F Charte. | 2019 |
| Label noise filtering techniques to improve monotonic classification | JR Cano, J Luengo, S García. | 2019 |
| Monotonic classification: An overview on algorithms, performance measures and data sets | JR Cano, PA Gutiérrez, B Krawczyk, M Woźniak, S García. | 2019 |
| Enabling smart data: noise filtering in big data classification | D García-Gil, J Luengo, S García, F Herrera. | 2019 |
| 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 |
| Chain based sampling for monotonic imbalanced classification | S González, S García, ST Li, F Herrera. | 2019 |
| DPASF: a flink library for streaming data preprocessing | A Alcalde-Barros, D García-Gil, S García, F Herrera. | 2019 |
| Clinical Cases-Masses and sources of emboli: rare and dramatic cases I | C Minguito Carazo, S Del Castillo Garcia, T De Benito Gonzalez, .... | 2019 |
| Poster session 1 | M Mathelie-Guinlet, A Reynaud, M Michaud, M Dijos, C Alexandrino, .... | 2019 |
| Adaptive cooperation of multi-swarm particle swarm optimizer-based hidden Markov model | A El Afia, O Aoun, S Garcia. | 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 |
| From Big to Smart Data: Iterative Ensemble Filter for Noise Filtering in Big Data classification | FH Diego Garcia, Francisco Luque-Sánchez, Julián Luengo, Salvador García. | 2019 |
| Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI | AB Arrieta, N Díaz-Rodríguez, J Del Ser, A Bennetot, S Tabik, A Barbado, .... | 2019 |
| Preprocessing methodology for time series: an industrial world application case study | JA Cortés-Ibáñez, S González, JJ Valle-Alonso, J Luengo, S García, .... | 2019 |
| Big Data Preprocessing as the Bridge between Big Data and Smart Data: BigDaPSpark and BigDaPFlink Libraries | D Garcıa-Gil, A Alcalde-Barros, J Luengo, S Garcıa, F Herrera. | 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 |
| Big Data Preprocessing as the Bridge between Big Data and Smart Data: BigDaPSpark and BigDaPFlink Libraries. | D García-Gil, A Alcalde-Barros, J Luengo, S García, F Herrera. | 2019 |
| Gil-L opez | AB Arrieta, N Díaz-Rodríguez, J Del Ser, A Bennetot, S Tabik, A Barbado, .... | 2019 |
| A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines | D Charte, F Charte, S García, MJ del Jesus, F Herrera. | 2018 |
| A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations | D Charte, F Charte, S García, F Herrera. | 2018 |
| A First Attempt on Monotonic Training Set Selection | JR Cano, S García. | 2018 |
| 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 |
| A distributed evolutionary multivariate discretizer for big data processing on apache spark | S Ramírez-Gallego, S García, JM Benítez, F Herrera. | 2018 |
| SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary | A Fernandez, S Garcia, F Herrera, NV Chawla. | 2018 |
| Big data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce | S Ramírez-Gallego, A Fernández, S García, M Chen, F Herrera. | 2018 |
| Imbalance: oversampling algorithms for imbalanced classification in R | I Cordón, S García, A Fernández, F Herrera. | 2018 |
| Dynamic ensemble selection for multi-class imbalanced datasets | S García, ZL Zhang, A Altalhi, S Alshomrani, F Herrera. | 2018 |
| Learning from Imbalanced Data Sets | A Fernández, S García, M Galar, RC Prati, B Krawczyk, F Herrera. | 2018 |
| Principal components analysis random discretization ensemble for big data | D García-Gil, S Ramírez-Gallego, S García, F Herrera. | 2018 |
| DRCW-ASEG: One-versus-One distance-based relative competence weighting with adaptive synthetic example generation for multi-class imbalanced datasets | ZL Zhang, XG Luo, S González, S García, F Herrera. | 2018 |
| Mc2esvm: multiclass classification based on cooperative evolution of support vector machines | A Rosales-Pérez, S García, H Terashima-Marin, CAC Coello, F Herrera. | 2018 |
| Online entropy-based discretization for data streaming classification | S Ramírez-Gallego, S García, F Herrera. | 2018 |
| Self inertia weight adaptation for the particle swarm optimization | O Aoun, A El Afia, S Garcia. | 2018 |
| A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms and Software | JL Suárez, S García, F Herrera. | 2018 |
| A snapshot on nonstandard supervised learning problems: taxonomy, relationships and methods | D Charte, F Charte, S García, F Herrera. | 2018 |
| Incidence and prognostic implications of late bleeding events after percutaneous mitral valve repair | T Benito-González, R Estévez-Loureiro, AP de Prado, C Minguito-Carazo, .... | 2018 |
| OCAPIS: R package for Ordinal Classification and Preprocessing in Scala | MC Heredia-Gómez, S García, PA Gutiérrez, F Herrera. | 2018 |
| P5589 Medical therapy versus medical and invasive therapies for elderly patients with multivessel coronary artery disease and left ventricular systolic dysfunction | C Minguito Carazo, S Del Castillo Garcia, I Iglesias Garriz, .... | 2018 |
| P2584 Changes in left ventricular function after percutaneous mitral valve repair with Mitraclip device: prognosis impact of speckle tracking echocardiography | T Benito Gonzalez, R Estevez-Loureiro, C Garrote Coloma, .... | 2018 |
| Medical therapy versus medical and invasive therapies for elderly patients with multivessel coronary artery disease and left ventricular systolic dysfunction | C Minguito Carazo, S Del Castillo Garcia, I Iglesias Garriz, .... | 2018 |
| Changes in left ventricular function after percutaneous mitral valve repair with Mitraclip device: prognosis impact of speckle tracking echocardiography | T Benito Gonzalez, R Estevez-Loureiro, C Garrote Coloma, .... | 2018 |
| Cooperative multi-objective evolutionary support vector machines for multiclass problems | A Rosales-Pérez, AE Gutierrez-Rodríguez, S García, H Terashima-Marín, .... | 2018 |
| On the Use of Random Discretization and Dimensionality Reduction in Ensembles for Big Data | D García-Gil, S Ramírez-Gallego, S García, F Herrera. | 2018 |
| Unusual vascular access for ECMO implantation after a complication during PCI | MG Ascencio Lemus, P Maiorano, C Minguito Carazo, L Castillo Pardo, .... | 2018 |
| BELIEF: A distance-based redundancy-proof feature selection method for Big Data | S Ramírez-Gallego, S García, N Xiong, F Herrera. | 2018 |
| Medical therapy versus medical and invasive therapies for elderly patients with multivessel coronary artery disease and left ventricular systolic dysfunction: P5589 | M Carazo, SDC Garcia, I Garriz, R Santamarta, TDB Gonzalez, A Lemus, .... | 2018 |
| Changes in left ventricular function after percutaneous mitral valve repair with Mitraclip device: prognosis impact of speckle tracking echocardiography: P2584 | B Gonzalez, R Estevez-Loureiro, G Coloma, T Sanz, A Rodriguez, .... | 2018 |
| A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms, Experimental Analysis, Prospects and Challenges (with Appendices on Mathematical Background and … | JL Suárez-Díaz, S García, F Herrera. | 2018 |
| Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial | AF Hernandez, JB Green, S Janmohamed, RB D'Agostino Sr, CB Granger, .... | 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 |
| CommuniMents: A framework for detecting community based sentiments for events | MA Jarwar, RA Abbasi, M Mushtaq, O Maqbool, NR Aljohani, A Daud, .... | 2017 |
| Prototype selection to improve monotonic nearest neighbor | JR Cano, NR Aljohani, RA Abbasi, JS Alowidbi, S Garcia. | 2017 |
| MoNGEL: monotonic nested generalized exemplar learning | J García, HM Fardoun, DM Alghazzawi, JR Cano, S García. | 2017 |
| Training set selection for monotonic ordinal classification | JR Cano, S García. | 2017 |
| Exact fuzzy k-nearest neighbor classification for big datasets | J Maillo, J Luengo, S García, F Herrera, I Triguero. | 2017 |
| Evolutionary Fuzzy Rule-Based Methods for Monotonic Classification | J Alcala-Fdez, R Alcala, S Gonzalez, Y NOJIMA, S Garcia. | 2017 |
| Nearest neighbor classification for high-speed big data streams using spark | S Ramírez-Gallego, B Krawczyk, S García, M Woźniak, JM Benítez, .... | 2017 |
| Minutiae-based fingerprint matching decomposition: methodology for big data frameworks | D Peralta, S García, JM Benitez, F Herrera. | 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 |
| Fast case-based reasoning for large-scale streaming classification | S Ramfrez-Gallego, B Krawczyk, S Garcia, M Wozniak, JM Benítez, .... | 2017 |
| A survey on data preprocessing for data stream mining: Current status and future directions | S Ramírez-Gallego, B Krawczyk, S García, M Woźniak, F Herrera. | 2017 |
| A comparison on scalability for batch big data processing on Apache Spark and Apache Flink | D García-Gil, S Ramírez-Gallego, S García, F Herrera. | 2017 |
| An evolutionary multiobjective model and instance selection for support vector machines with pareto-based ensembles | A Rosales-Pérez, S García, JA Gonzalez, CAC Coello, F Herrera. | 2017 |
| Class switching according to nearest enemy distance for learning from highly imbalanced data-sets | S Gónzalez, S García, M Lázaro, AR Figueiras-Vidal, F Herrera. | 2017 |
| Exploring the effectiveness of dynamic ensemble selection in the one-versus-one scheme | ZL Zhang, XG Luo, S García, JF Tang, F Herrera. | 2017 |
| rnpbst: An R package covering non-parametric and bayesian statistical tests | J Carrasco, S García, M del Mar Rueda, F Herrera. | 2017 |
| Cost-Sensitive back-propagation neural networks with binarization techniques in addressing multi-class problems and non-competent classifiers | ZL Zhang, XG Luo, S García, F Herrera. | 2017 |
| P2728Short-term mortality on ST-segment myocardial infarction after the implementation of a rapid access system to reperfusion | MG Ascencio Lemus, I Iglesias Garriz, I Prieto Salvador, .... | 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 |
| Hyperrectangles selection for monotonic classification by using evolutionary algorithms | J García, AM AlBar, NR Aljohani, JR Cano, S García. | 2016 |
| A Nearest Hyperrectangle Monotonic Learning Method | J García, JR Cano, S García. | 2016 |
| Tutorial on practical tips of the most influential data preprocessing algorithms in data mining | S García, J Luengo, F Herrera. | 2016 |
| Big data preprocessing: methods and prospects | S García, S Ramírez-Gallego, J Luengo, JM Benítez, F Herrera. | 2016 |
| 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 |
| Data discretization: taxonomy and big data challenge | S Ramírez‐Gallego, S García, H Mouriño‐Talín, D Martínez‐Rego, .... | 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 |
| A Wrapper Evolutionary Approach for Supervised Multivariate Discretization: A Case Study on Decision Trees | S Ramírez-Gallego, S García, JM Benítez, F Herrera. | 2016 |
| Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data | Z Zhang, B Krawczyk, S Garcìa, A Rosales-Pérez, F Herrera. | 2016 |
| Evolutionary fuzzy k-nearest neighbors algorithm using interval-valued fuzzy sets | J Derrac, F Chiclana, S García, F Herrera. | 2016 |
| Evolutionary wrapper approaches for training set selection as preprocessing mechanism for support vector machines: Experimental evaluation and support vector analysis | N Verbiest, J Derrac, C Cornelis, S García, F Herrera. | 2016 |
| Early IL-1 signaling promotes iBALT induction after influenza virus infection | K Neyt, CH GeurtsvanKessel, K Deswarte, H Hammad, BN Lambrecht. | 2016 |
| Current prospects on ordinal and monotonic classification | PA Gutiérrez, S García. | 2016 |
| Landmark-based music recognition system optimisation using genetic algorithms | S Gutiérrez, S García. | 2016 |
| Managing monotonicity in classification by a pruned adaboost | S González, F Herrera, S García. | 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 |
| 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 |
| Multivariate discretization based on evolutionary cut points selection for classification | S Ramírez-Gallego, S García, JM Benítez, F Herrera. | 2015 |
| Distributed entropy minimization discretizer for big data analysis under apache spark | S Ramírez-Gallego, S García, H Mouriño-Talín, D Martínez-Rego, .... | 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 |
| 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 |
| Monotonic random forest with an ensemble pruning mechanism based on the degree of monotonicity | S González, F Herrera, S García. | 2015 |
| Javanpst: Nonparametric statistical tests in java | J Derrac, S García, F Herrera. | 2015 |
| Managing monotonicity in classification by a pruned random forest | S González, F Herrera, S García. | 2015 |
| An interval valued k-nearest neighbors classifier | J Derrac, F Chiclana, S Garcia, F Herrera. | 2015 |
| Trypanosoma cruziPCR: a useful tool for the control of therapeutic failure? | E Sulleiro, N Serre, B Treviño, S García, S González, M Espasa, Z Moure, .... | 2015 |
| Estudio descriptivo de la población de usuarios hipotiroideos de ASSE, de la región del Santoral Canelones, en el año 2015, y su posible relación con el uso de agroquímicos. | K Csigi, M Duarte, S García, S González, V Guido. | 2015 |
| Trypanosoma cruziPCR: a useful tool for the control of therapeutic failure?: PS2. 082 | E Sulleiro, N Serre, B Treviño, S García, S González, M Espasa, Z Moure, .... | 2015 |
| A survey on fingerprint minutiae-based local matching for | D Peralta, M Galar, I Triguero, D Paternain, S García, E Barrenechea, .... | 2015 |
| Trypanosoma cruzi PCR: a useful tool for the control of therapeutic failure? | E Sulleiro, N Serre, B Trevino, S Garcia, S Gonzalez, M Espasa, Z Moure, .... | 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 |
| Applying Subgroup Discovery Based on Evolutionary Fuzzy Systems for Web Usage Mining in E-Commerce: A Case Study on OrOliveSur. com | CJ Carmona, MJ del Jesus, S García. | 2014 |
| Data Preprocessing in Data Mining | S García, J Luengo, 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 |
| Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects | J Derrac, S García, F Herrera. | 2014 |
| Analyzing convergence performance of evolutionary algorithms: a statistical approach | J Derrac, S García, S Hui, PN Suganthan, 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 |
| Website Design As Moderating Factor of Online User Behavior | JMA Pilar, SDB García. | 2014 |
| Improving Disease Prediction using Unlabeled and Synthetic Samples | I Triguero, S García, F Herrera, Y Saeys. | 2014 |
| Chaotic lid-driven square cavity flows at extreme Reynolds numbers | S Garcia. | 2014 |
| On the statistical analysis of the parameters’ trend in a machine learning algorithm | S García, J Derrac, S Ramírez-Gallego, F Herrera. | 2014 |
| Journal: Intelligent Systems Reference Library Data Preprocessing in Data Mining, 2014, p. 1-17 | S García, J Luengo, F Herrera. | 2014 |
| An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics | V López, A Fernández, S García, V Palade, F Herrera. | 2013 |
| Class imbalance learning methods for support vector machines | R Batuwita, V Palade. | 2013 |
| On the use of evolutionary feature selection for improving fuzzy rough set based prototype selection | J Derrac, N Verbiest, S García, C Cornelis, F Herrera. | 2013 |
| Statistical analysis of convergence performance throughout the evolutionary search: A case study with SaDE-MMTS and Sa-EPSDE-MMTS | J Derrac, S Garcia, S Hui, F Herrera, PN Suganthan. | 2013 |
| Web usage mining to improve the design of an e-commerce website: OrOliveSur. com | CJ Carmona, S Ramírez-Gallego, F Torres, E Bernal, MJ del Jesús, .... | 2012 |
| Evolutionary-based selection of generalized instances for imbalanced classification | S Garcı, I Triguero, CJ Carmona, F Herrera. | 2012 |
| A Preliminary Study on Selecting the Optimal Cut Points in Discretization by Evolutionary Algorithms. | S García, V López, J Luengo, CJ Carmona, F Herrera. | 2012 |
| A survey of discretization techniques: Taxonomy and empirical analysis in supervised learning | S Garcia, J Luengo, JA Sáez, V Lopez, F Herrera. | 2012 |
| On the choice of the best imputation methods for missing values considering three groups of classification methods | J Luengo, S García, F Herrera. | 2012 |
| A taxonomy and experimental study on prototype generation for nearest neighbor classification | I Triguero, J Derrac, S Garcia, F Herrera. | 2012 |
| Enhancing Evolutionary Instance Selection Algorithms by means of Fuzzy Rough Set based Feature Selection | J Derrac, C Cornelis, S García, 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 |
| EL TIPO DE INCENTIVO COMO DETERMINANTE EN EL ATRACTIVO DE LA PROMOCIÓN DE VENTA EN TURISMO RURAL. EFECTO MODERADOR DEL SEXO, LA EDAD Y LA EXPERIENCIA | SDB García, LL Moreno, DMF Jamilena. | 2012 |
| Análisis cross-cultural de la aceptación de los sitios web | JM Alcántara-Pilar, S Barrio-García, E Crespo-Almendros. | 2012 |
| Subgroup Discovery Applied to the e-Commerce Website OrOliveSur. com. | CJ Carmona, S Ramírez-Gallego, FJ Torres, E Bernal, MJ del Jesús, .... | 2012 |
| Right ventricular systolic function | M Altman, C Bergerot, H Thibault, A Aussoleil, E Skuldadt Davidsen, .... | 2012 |
| An extension on | S Garcia, F Herrera. | 2012 |
| Joaquin Derrac, Jose Cano, and Francisco Herrera. Prototype selection for nearest neighbor classification: Taxonomy and empirical study | S Garcia. | 2012 |
| Evolutionary selection of hyperrectangles in nested generalized exemplar learning | S García, J Derrac, J Luengo, CJ Carmona, F Herrera. | 2011 |
| Prototype selection for nearest neighbor classification: Taxonomy and empirical study | S Garcia, J Derrac, JR Cano, F Herrera. | 2011 |
| Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. | J Alcalá-Fdez, A Fernández, J Luengo, J Derrac, S García, L Sánchez, .... | 2011 |
| Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling | J Luengo, A Fernández, S García, F Herrera. | 2011 |
| Using KEEL software as a educational tool: A case of study teaching data mining | J Derrac, J Luengo, J Alcala-Fdez, A Fernandez, S Garcia. | 2011 |
| Soft Computing Techniques in Data Mining. | J Alcalá-Fdez, F Herrera, H Ishibuchi, Y Kaisho, Y Nojima, BC Chien, .... | 2011 |
| KEEL data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework | J Alcala-Fdez, A Fernández, J Luengo, J Derrac, S García, L Sánchez, .... | 2011 |
| Addressing the classification with imbalanced data: open problems and new challenges on class distribution | A Fernández, S García, F Herrera. | 2011 |
| A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms | J Derrac, S García, D Molina, F Herrera. | 2011 |
| 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 |
| A preliminary study on the use of fuzzy rough set based feature selection for improving evolutionary instance selection algorithms | J Derrac, C Cornelis, S García, F Herrera. | 2011 |
| Three-way decisions with probabilistic rough sets. | Q Duan, Y Chen, D Miao, R Wang, K Wu, J Derrac, C Cornelis, S Garcia, .... | 2011 |
| Addressing the Classification with Imbalanced Data: Open Problems and New Challenges on Class Distribution. | A Fernández, S García, 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 |
| A Review on Evolutionary Prototype Selection: An Empirical Study of Performance and Efficiency | S García, JR Cano, F Herrera. | 2010 |
| Introduction to the experimental design in the data mining tool KEEL | J Alcalá-Fdez, F Herrera, S García, MJ del Jesus, L Sánchez, .... | 2010 |
| Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power | S García, A Fernández, J Luengo, F Herrera. | 2010 |
| Genetics-based machine learning for rule induction: state of the art, taxonomy, and comparative study | A Fernández, S García, J Luengo, E Bernadó-Mansilla, F Herrera. | 2010 |
| A study on the use of imputation methods for experimentation with Radial Basis Function Network classifiers handling missing attribute values: The good synergy between RBFNs … | J Luengo, S García, F Herrera. | 2010 |
| Geneticsbased machine learning for rule induction: Taxonomy, experimental study and state of the art | A Fernández, S García, J Luengo, E Bernadó-Mansilla, F Herrera. | 2010 |
| Special issue on Hybrid Fuzzy Models | JM Benítez, S García, S Caballé, ÁA Juan. | 2010 |
| A survey on evolutionary instance selection and generation | J Derrac, S García, F Herrera. | 2010 |
| IFS-CoCo: Instance and feature selection based on cooperative coevolution with nearest neighbor rule | J Derrac, S García, F Herrera. | 2010 |
| IPADE: Iterative prototype adjustment for nearest neighbor classification | I Triguero, S García, F Herrera. | 2010 |
| Stratified prototype selection based on a steady-state memetic algorithm: a study of scalability | J Derrac, 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 |
| IFS-CoCo in the landscape contest: description and results | J Derrac, S García, F Herrera. | 2010 |
| Genetics-based machine learning for rule induction: Taxonomy, experimental study and state of the art | A Fernández, S García, J Luengo, E Bernadó-Mansilla, F Herrera. | 2010 |
| Gold heap leach simulation and optimization using a multiphysics model | S Garcia, C Ramon, A Esplin, JE Gebhardt, A Hernandez, D McBride, .... | 2010 |
| KEEL: a software tool to assess evolutionary algorithms for data mining problems | J Alcalá-Fdez, L Sanchez, S Garcia, MJ del Jesus, S Ventura, JM Garrell, .... | 2009 |
| Diagnose effective evolutionary prototype selection using an overlapping measure | S Garcia, JR Cano, E Bernado-Mansilla, F Herrera. | 2009 |
| A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability | S García, A Fernández, J Luengo, F Herrera. | 2009 |
| A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests | J Luengo, S García, F Herrera. | 2009 |
| A first approach to nearest hyperrectangle selection by evolutionary algorithms | S García, J Derrac, J Luengo, F Herrera. | 2009 |
| Addressing data-complexity for imbalanced data-sets: A preliminary study on the use of preprocessing for c4. 5 | J Luengo, A Fernández, S García, F Herrera. | 2009 |
| Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems | S García, A Fernández, F Herrera. | 2009 |
| 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 | S García, D Molina, M Lozano, F Herrera. | 2009 |
| Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy | S García, F Herrera. | 2009 |
| A first study on the use of coevolutionary algorithms for instance and feature selection | J Derrac, S García, F Herrera. | 2009 |
| Computational intelligence | S Artit. | 2009 |
| A Study of | S Garcia, A Fernandez, G Luengo, F Herrera. | 2009 |
| A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets | A Fernández, S García, MJ del Jesus, F Herrera. | 2008 |
| A memetic algorithm for evolutionary prototype selection: A scaling up approach | S García, JR Cano, F Herrera. | 2008 |
| Subgroup discover in large size data sets preprocessed using stratified instance selection for increasing the presence of minority classes | JR Cano, S García, F Herrera. | 2008 |
| KEEL: A data mining software tool integrating genetic fuzzy systems | J Alcalá-Fdez, S García, FJ Berlanga, A Fernández, L Sánchez, .... | 2008 |
| Making CN2-SD subgroup discovery algorithm scalable to large size data sets using instance selection | JR Cano, F Herrera, M Lozano, S García. | 2008 |
| An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons | S García, F Herrera. | 2008 |
| Evolutionary training set selection to optimize c4. 5 in imbalanced problems | S García, F Herrera. | 2008 |
| Design of experiments in computational intelligence: on the use of statistical inference | S García, F Herrera. | 2008 |
| An extension on" statistical comparisons of classifiers over multiple data sets" for all pairwise comparisons | S García López, F Herrera. | 2008 |
| Soft Comput | J Alcalá-fdez, L Sánchez, S García, MJ Jesus, S Ventura, JM Garrell, .... | 2008 |
| An analysis of the rule weights and fuzzy reasoning methods for linguistic rule based classification systems applied to problems with highly imbalanced data sets | A Fernández, S García, F Herrera, MJ del Jesús. | 2007 |
| A study on the use of the fuzzy reasoning method based on the winning rule vs. voting procedure for classification with imbalanced data sets | A Fernández, S García, MJ Del Jesús, F Herrera. | 2007 |
| Fuzzy Machine Learning-An Analysis of the Rule Weights and Fuzzy Reasoning Methods for Linguistic Rule Based Classification Systems Applied to Problems with Highly Imbalanced … | A Fernandez, S Garcia, F Herrera, MJ Jesus. | 2007 |
| A study on the use of statistical tests for experimentation with neural networks | J Luengo, S García, F Herrera. | 2007 |
| Statistical comparisons by means of non-parametric tests: a case study on genetic based machine learning | S García, AD Benítez, F Herrera, A Fernández. | 2007 |
| Un estudio experimental sobre el uso de test no paramétricos para analizar el comportamiento de los algoritmos evolutivos en problemas de optimización | S García, D Molina, M Lozano, F Herrera. | 2007 |
| Tests no paramétricos de comparaciones múltiples con algoritmo de control en el análisis de algoritmos evolutivos: Un caso de estudio con los resultados de la sesión especial … | S García, D Molina, M Lozano, F Herrera. | 2007 |
| The lid-driven square cavity flow: from stationary to time periodic and chaotic | S Garcia. | 2007 |
| Analysis of evolutionary prototipe selection by means of a data complexity measure based on class separability | JR Cano, S García, F Herrera, EB Mansilla. | 2007 |
| A proposal of evolutionary prototype selection for class imbalance problems | S García, JR Cano, A Fernández, F Herrera. | 2006 |
| Incorporating knowledge in evolutionary prototype selection | S García, JR Cano, F Herrera. | 2006 |
| UN PRIMER ESTUDIO SOBRE EL USO DE LOS SISTEMAS DE CLASIFICACIÓN BASADOS EN REGLAS DIFUSAS EN PROBLEMAS DE CLASIFICACIÓN CON CLASES NO BALANCEADAS | AF Hilario, S García, F Herrera, MJ del Jesus. | 2006 |
| Terpsícore montañesa. Bailes y bailarines en el Santander decimonónico | S GARCÍA. | 2005 |
| Caracterización ampelográfica de la variedad tinta de vid Rufete (Vitis vinifera L.) en viñedos de la Sierra de Francia (Salamanca) | S García, CA Hernández, AM Sanz, JAR Cano. | 2005 |
| Oro-and nasopharyngeal suction of meconium-stained neonates before delivery of their shoulders does not prevent meconium aspiration syndrome: Results of the international … | N Vain, E Szyld, L Prudent, T Wiswell, A Aguilar, N Vivas, S Garcia, .... | 2002 |
| Recomendaciones para uso de CPAP en recién nacidos pretérmino | A Dinerstein, N Vivas, P Bellani, P Crispino, G Echebarrena, S García, .... | 2001 |
| Observer dependent deformations in illustration | D Martín, S García, JC Torres. | 2000 |
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