9 citations · 15 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
Combined Cleaning and Resampling Algorithm for Multi-Class Imbalanced Data with Label Noise
Michał Koziarski, Michał Woźniak, Bartosz Krawczyk
The imbalanced data classification is one of the most crucial tasks facing modern data analysis. Especially when combined with other difficulty factors, such as the presence of noi…