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Enislay Ramentol
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2020 – today
- 2022
- [i1]Nico Weber, Patrick Holzer, Tania Jacob, Enislay Ramentol:
Fed-DART and FACT: A solution for Federated Learning in a production environment. CoRR abs/2205.11267 (2022) - 2020
- [j6]Sahar Tahvili, Leo Hatvani, Enislay Ramentol, Rita Pimentel, Wasif Afzal, Francisco Herrera:
A novel methodology to classify test cases using natural language processing and imbalanced learning. Eng. Appl. Artif. Intell. 95: 103878 (2020)
2010 – 2019
- 2019
- [j5]Chongsheng Zhang, Jingjun Bi, Shixin Xu, Enislay Ramentol, Gaojuan Fan, Baojun Qiao, Hamido Fujita:
Multi-Imbalance: An open-source software for multi-class imbalance learning. Knowl. Based Syst. 174: 137-143 (2019) - 2016
- [j4]Enislay Ramentol, I. Gondres, S. Lajes, Rafael Bello, Yaile Caballero, Chris Cornelis, Francisco Herrera:
Fuzzy-rough imbalanced learning for the diagnosis of High Voltage Circuit Breaker maintenance: The SMOTE-FRST-2T algorithm. Eng. Appl. Artif. Intell. 48: 134-139 (2016) - 2015
- [j3]Enislay Ramentol, Sarah Vluymans, Nele Verbiest, Yaile Caballero, Rafael Bello, Chris Cornelis, Francisco Herrera:
IFROWANN: Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor Classification. IEEE Trans. Fuzzy Syst. 23(5): 1622-1637 (2015) - 2014
- [j2]Nele Verbiest, Enislay Ramentol, Chris Cornelis, Francisco Herrera:
Preprocessing noisy imbalanced datasets using SMOTE enhanced with fuzzy rough prototype selection. Appl. Soft Comput. 22: 511-517 (2014) - 2012
- [j1]Enislay Ramentol, Yaile Caballero, Rafael Bello, Francisco Herrera:
SMOTE-RSB *: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory. Knowl. Inf. Syst. 33(2): 245-265 (2012) - [c1]Nele Verbiest, Enislay Ramentol, Chris Cornelis, Francisco Herrera:
Improving SMOTE with Fuzzy Rough Prototype Selection to Detect Noise in Imbalanced Classification Data. IBERAMIA 2012: 169-178 - 2010
- [p1]Yaile Caballero, Rafael Bello, Leticia Arco, María M. García, Enislay Ramentol:
Knowledge Discovery Using Rough Set Theory. Advances in Machine Learning I 2010: 367-383
Coauthor Index
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