9 citations · 15 across the 7 of their papers we have counts for
4 papers · 1 filter
On the combined effect of class imbalance and concept complexity in deep learning
Kushankur Ghosh, Colin Bellinger, Roberto Corizzo +2
Structural concept complexity, class overlap, and data scarcity are some of the most important factors influencing the performance of classifiers under class imbalance conditions.…
DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data
Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla
Despite over two decades of progress, imbalanced data is still considered a significant challenge for contemporary machine learning models. Modern advances in deep learning have ma…
Class-Incremental Experience Replay for Continual Learning under Concept Drift
Łukasz Korycki, Bartosz Krawczyk
Modern machine learning systems need to be able to cope with constantly arriving and changing data. Two main areas of research dealing with such scenarios are continual learning an…
Concept Drift Detection from Multi-Class Imbalanced Data Streams
Łukasz Korycki, Bartosz Krawczyk
Continual learning from data streams is among the most important topics in contemporary machine learning. One of the biggest challenges in this domain lies in creating algorithms t…