Profil Dr. Enislay Ramentol


  • Maschinelles Lernen mit Fokus auf Imbalanced Learning
  • Wissensbasiertes System/Intelligentes System
  • Unsicherheitsmanagement bei Klassifizierungsproblemen
  • Rough Set und Fuzzy Rough Set Theorie




  • Ramentol, E.; Zhang, C.; Bi, J.; Xu, S.; Fan, G.; Qiao, B.:
    Multi-Imbalance: An open-source software for multi-class imbalance learning.
    Knowledge-Based Systems, Vol. 174, 137-143 (2019).
  • Ramentol, E.; Gondres, S.; Lajes, S.; Bello, R.; Caballero, Y.; Cornelis, C.; Herrera, F.:
    Fuzzy-rough imbalanced learning for the diagnosis of High Voltage Circuit Breaker maintenance: The SMOTE-FRST-2T algorithm.
    Engineering Applications of Artificial Intelligence, Vol. 48, 134-139 (2016).
  • Ramentol, E.; Vluymans, S.; Verbiest, N.; Caballero, Y.; Bello, R.; Cornelis, C.; Herrera, F.:
    IFROWANN: Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor Classification.
    IEEE Trans. Fuzzy Systems, Vol. 23(5), 1622-1637 (2015).
  • Ramentol, E.; Verbiest, N.; Cornelis, C.; Herrera, F.:
    Preprocessing noisy imbalanced datasets using SMOTE enhanced with fuzzy rough prototype selection.
    Applied Soft Computing, Vol. 22, 511-517 (2014).
  • Ramentol, E.; Caballero, Y.; Bello, R.; Herrera, F.:
    SMOTE-RSB*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory.
    Knowledge and Information Systems, Vol. 33(2), 245-265 (2012).