Ordinal regression methods: survey and experimental study PA Gutiérrez, M Pérez-Ortiz, J Sánchez-Monedero, F Fernández-Navarro, ... IEEE Transactions on Knowledge and Data Engineering 28 (1), 127-146, 2016 | 472 | 2016 |
Object-Based Image Classification of Summer Crops with Machine Learning Methods JM Peña, PA Gutiérrez, C Hervás-Martínez, J Six, RE Plant, ... Remote Sensing 6 (6), 5019-5041, 2014 | 229 | 2014 |
A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method M Pérez-Ortiz, JM Peña, PA Gutiérrez, J Torres-Sánchez, ... Applied Soft Computing 37, 533-544, 2015 | 217 | 2015 |
Selecting patterns and features for between-and within-crop-row weed mapping using UAV-imagery M Pérez-Ortiz, JM Peña, PA Gutiérrez, J Torres-Sánchez, ... Expert Systems with Applications 47, 85-94, 2016 | 201 | 2016 |
Sensitivity versus accuracy in multiclass problems using memetic pareto evolutionary neural networks JC Fernández, FJ Martínez-Estudillo, C Hervás, PA Gutiérrez IEEE Transactions on Neural Networks 21 (5), 750-770, 2010 | 188 | 2010 |
A dynamic over-sampling procedure based on sensitivity for multi-class problems F Fernández-Navarro, C Hervás-Martínez, P Antonio Gutiérrez Pattern Recognition 44 (8), 1821-1833, 2011 | 169 | 2011 |
A Review of Classification Problems and Algorithms in Renewable Energy Applications M Pérez-Ortiz, S Jiménez-Fernández, PA Gutiérrez, E Alexandre, ... Energies 9 (8), 607, 2016 | 152 | 2016 |
Metrics to guide a multi-objective evolutionary algorithm for ordinal classification M Cruz-Ramírez, C Hervás-Martínez, J Sánchez-Monedero, PA Gutiérrez Neurocomputing 135, 21-31, 2014 | 111 | 2014 |
Evolutionary product-unit neural networks classifiers FJ Martínez-Estudillo, C Hervás-Martínez, PA Gutiérrez, ... Neurocomputing 72 (1-3), 548-561, 2008 | 105 | 2008 |
Monotonic classification: An overview on algorithms, performance measures and data sets JR Cano, PA Gutiérrez, B Krawczyk, M Woźniak, S García Neurocomputing 341, 168-182, 2019 | 98 | 2019 |
Logistic regression by means of evolutionary radial basis function neural networks PA Gutiérrez, C Hervás-Martínez, FJ Martínez-Estudillo IEEE Transactions on Neural Networks 22 (2), 246-263, 2011 | 92 | 2011 |
MELM-GRBF: A modified version of the extreme learning machine for generalized radial basis function neural networks F Fernández-Navarro, C Hervás-Martínez, J Sanchez-Monedero, ... Neurocomputing 74 (16), 2502-2510, 2011 | 90 | 2011 |
Evolutionary artificial neural networks for accurate solar radiation prediction D Guijo-Rubio, AM Durán-Rosal, PA Gutiérrez, AM Gómez-Orellana, ... Energy 210, 118374, 2020 | 89 | 2020 |
Machine learning methods for binary and multiclass classification of melanoma thickness from dermoscopic images A Sáez, J Sánchez-Monedero, PA Gutiérrez, C Hervás-Martínez IEEE transactions on medical imaging 35 (4), 1036-1045, 2015 | 80 | 2015 |
Oversampling the minority class in the feature space M Pérez-Ortiz, PA Gutiérrez, P Tino, C Hervás-Martínez IEEE transactions on neural networks and learning systems 27 (9), 1947-1961, 2015 | 74 | 2015 |
Graph-Based Approaches for Over-sampling in the context of Ordinal Regression M Perez-Ortiz, P Gutierrez, C Hervas Martinez, X Yao IEEE Transactions on Knowledge and Data Engineering 27 (5), 1233-1245, 2015 | 74 | 2015 |
Significant wave height and energy flux range forecast with machine learning classifiers JC Fernández, S Salcedo-Sanz, PA Gutiérrez, E Alexandre, ... Engineering Applications of Artificial Intelligence 43, 44-53, 2015 | 73 | 2015 |
Logistic regression product-unit neural networks for mapping Ridolfia segetum infestations in sunflower crop using multitemporal remote sensed data PA Gutiérrez, F López-Granados, JM Peña-Barragán, M Jurado-Expósito, ... computers and electronics in agriculture 64 (2), 293-306, 2008 | 71 | 2008 |
Multi-task learning for the prediction of wind power ramp events with deep neural networks M Dorado-Moreno, N Navarin, PA Gutiérrez, L Prieto, A Sperduti, ... Neural Networks 123, 401-411, 2020 | 69 | 2020 |
Machine learning regression and classification methods for fog events prediction C Castillo-Botón, D Casillas-Pérez, C Casanova-Mateo, S Ghimire, ... Atmospheric Research 272, 106157, 2022 | 62 | 2022 |