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Enislay Ramentol
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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
5072012
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
1482019
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
1072014
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
1032014
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
812016
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
552012
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
372020
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
342012
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
232021
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
162022
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
72010
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
42019
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
32009
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
22022
Machine learning models for industrial applications
E Ramentol, T Olsson, S Barua
AI and Learning Systems-Industrial Applications and Future Directions, 2021
12021
Short-Term Air Pollution Forecasting Using Embeddings in Neural Networks
E Ramentol, S Grimm, M Stinzendörfer, A Wagner
Atmosphere 14 (2), 298, 2023
2023
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
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Articles 1–19