Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWMSchwerpunkte/Kompetenzen
- Maschinelles Lernen mit Fokus auf Imbalanced Learning
- Wissensbasiertes System/Intelligentes System
- Unsicherheitsmanagement bei Klassifizierungsproblemen
- Rough Set und Fuzzy Rough Set Theorie
Publikationen
Highlightpublikationen
- 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).