9 citations · 15 across the 7 of their papers we have counts for
7 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.…
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…
Adaptive Deep Forest for Online Learning from Drifting Data Streams
Łukasz Korycki, Bartosz Krawczyk
Learning from data streams is among the most vital fields of contemporary data mining. The online analysis of information coming from those potentially unbounded data sources allow…
Adversarial Concept Drift Detection under Poisoning Attacks for Robust Data Stream Mining
Łukasz Korycki, Bartosz Krawczyk
Continuous learning from streaming data is among the most challenging topics in the contemporary machine learning. In this domain, learning algorithms must not only be able to hand…
Instance exploitation for learning temporary concepts from sparsely labeled drifting data streams
Łukasz Korycki, Bartosz Krawczyk
Continual learning from streaming data sources becomes more and more popular due to the increasing number of online tools and systems. Dealing with dynamic and everlasting problems…