| SMOTE-RSB *: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets … E Ramentol, Y Caballero, R Bello, F Herrera Knowledge and information systems 33, 245-265, 2012 | 533 | 2012 |
| Multi-imbalance: An open-source software for multi-class imbalance learning C Zhang, J Bi, S Xu, E Ramentol, G Fan, B Qiao, H Fujita Knowledge-Based Systems 174, 137-143, 2019 | 157 | 2019 |
| IFROWANN: imbalanced fuzzy-rough ordered weighted average nearest neighbor classification E Ramentol, S Vluymans, N Verbiest, Y Caballero, R Bello, C Cornelis, ... IEEE Transactions on Fuzzy Systems 23 (5), 1622-1637, 2014 | 114 | 2014 |
| Preprocessing noisy imbalanced datasets using SMOTE enhanced with fuzzy rough prototype selection N Verbiest, E Ramentol, C Cornelis, F Herrera Applied Soft Computing 22, 511-517, 2014 | 108 | 2014 |
| Fuzzy-rough imbalanced learning for the diagnosis of High Voltage Circuit Breaker maintenance: The SMOTE-FRST-2T algorithm E Ramentol, I Gondres, S Lajes, R Bello, Y Caballero, C Cornelis, ... Engineering Applications of Artificial Intelligence 48, 134-139, 2016 | 88 | 2016 |
| SMOTE-FRST: a new resampling method using fuzzy rough set theory E Ramentol, N Verbiest, R Bello, Y Caballero, C Cornelis, F Herrera Uncertainty modeling in knowledge engineering and decision making, 800-805, 2012 | 56 | 2012 |
| A novel methodology to classify test cases using natural language processing and imbalanced learning S Tahvili, L Hatvani, E Ramentol, R Pimentel, W Afzal, F Herrera Engineering applications of artificial intelligence 95, 103878, 2020 | 43 | 2020 |
| Improving SMOTE with fuzzy rough prototype selection to detect noise in imbalanced classification data N Verbiest, E Ramentol, C Cornelis, F Herrera Advances in Artificial Intelligence–IBERAMIA 2012: 13th Ibero-American …, 2012 | 37 | 2012 |
| Short-and long-term forecasting of electricity prices using embedding of calendar information in neural networks A Wagner, E Ramentol, F Schirra, H Michaeli Journal of Commodity Markets 28, 100246, 2022 | 25 | 2022 |
| A data-driven approach for predicting long-term degradation of a fleet of micro gas turbines T Olsson, E Ramentol, M Rahman, M Oostveen, K Kyprianidis Energy and AI 4, 100064, 2021 | 25 | 2021 |
| Knowledge discovery using rough set theory Y Caballero, R Bello, L Arco, M García, E Ramentol Advances in Machine Learning I: Dedicated to the Memory of Professor Ryszard …, 2010 | 8 | 2010 |
| Early detection of possible undergraduate drop out using a new method based on probabilistic rough set theory E Ramentol, J Madera, A Rodríguez Uncertainty Management with Fuzzy and Rough Sets: Recent Advances and …, 2019 | 4 | 2019 |
| Machine learning models for industrial applications E Ramentol, T Olsson, S Barua AI and Learning Systems-Industrial Applications and Future Directions, 2021 | 3 | 2021 |
| Edición de Conjuntos de Entrenamiento no Balanceados, haciendo uso de Operadores Genéticos y la Teoría de los Conjuntos Aproximados E Ramentol, F Herrera, R Bello, Y Caballero, Y Sanchez Congreso Español sobre Metaheuristicas y Algoritmos Bioinspirados, 277-84, 2009 | 3 | 2009 |
| Short-Term Air Pollution Forecasting Using Embeddings in Neural Networks E Ramentol, S Grimm, M Stinzendörfer, A Wagner Atmosphere 14 (2), 298, 2023 | 2 | 2023 |
| Fed-DART and FACT: A solution for Federated Learning in a production environment N Weber, P Holzer, T Jacob, E Ramentol arXiv preprint arXiv:2205.11267, 2022 | 2 | 2022 |
| Chapter Machine Learning Models for Industrial Applications T Olsson, E Ramentol, S Barua IntechOpen, 2021 | | 2021 |
| Nuevos métodos de edición de conjuntos de entrenamiento no balanceados usando la teoría de los conjuntos aproximados E Ramentol Martínez Universidad de Granada, 2014 | | 2014 |
| SMOTE-FRST: Un nuevo método de remuestreo basado en la teoría de los Fuzzy Rough Set E Ramentol, N Verbiest, R Bello, Y Caballero, C Cornelis, F Herrera | | |
| Edición de Conjuntos de Entrenamiento no Balanceados, haciendo uso de Operadores Genéticos y la Teoría de los Conjuntos Aproximados ER Martínez, F Herrera, RB Pérez, YC Mota, YS López | | |
Francisco HerreraProfessor Computer Science and AI, DaSCI Research Institute, Granada University, Spainכתובת אימייל מאומתת בדומיין decsai.ugr.es
