Knowledge Discovery Using Rough Set Theory. - Yaile Caballero; Rafael Bello 001; Leticia Arco; María M. García; Enislay Ramentol (Advances in Machine Learning I, 2010)

Improving SMOTE with Fuzzy Rough Prototype Selection to Detect Noise in Imbalanced Classification Data. - Nele Verbiest; Enislay Ramentol; Chris Cornelis; Francisco Herrera (IBERAMIA, 2012)

SMOTE-RSB *: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory. - Enislay Ramentol; Yaile Caballero; Rafael Bello 001; Francisco Herrera (Knowl. Inf. Syst., 2012)

Preprocessing noisy imbalanced datasets using SMOTE enhanced with fuzzy rough prototype selection. - Nele Verbiest; Enislay Ramentol; Chris Cornelis; Francisco Herrera (Appl. Soft Comput., 2014)

IFROWANN: Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor Classification. - Enislay Ramentol; Sarah Vluymans; Nele Verbiest; Yaile Caballero; Rafael Bello 001; Chris Cornelis; Francisco Herrera (IEEE Trans. Fuzzy Syst., 2015)

Fuzzy-rough imbalanced learning for the diagnosis of High Voltage Circuit Breaker maintenance: The SMOTE-FRST-2T algorithm. - Enislay Ramentol; I. Gondres; S. Lajes; Rafael Bello 001; Yaile Caballero; Chris Cornelis; Francisco Herrera (Eng. Appl. Artif. Intell., 2016)

Multi-Imbalance: An open-source software for multi-class imbalance learning. - Chongsheng Zhang; Jingjun Bi; Shixin Xu; Enislay Ramentol; Gaojuan Fan; Baojun Qiao; Hamido Fujita (Knowl. Based Syst., 2019)

A novel methodology to classify test cases using natural language processing and imbalanced learning. - Sahar Tahvili; Leo Hatvani; Enislay Ramentol; Rita Pimentel; Wasif Afzal; Francisco Herrera (Eng. Appl. Artif. Intell., 2020)

Fed-DART and FACT: A solution for Federated Learning in a production environment. - Nico Weber; Patrick Holzer; Tania Jacob; Enislay Ramentol (CoRR, 2022)