92 citations · 328 across the 17 of their papers we have counts for
8 papers · 1 filter
Multi-Label Transfer Learning in Non-Stationary Data Streams
Honghui Du, Leandro Minku, Aonghus Lawlor +1
Label concepts in multi-label data streams often experience drift in non-stationary environments, either independently or in relation to other labels. Transferring knowledge betwee…
MARLINE: Multi-Source Mapping Transfer Learning for Non-Stationary Environments
Honghui Du, Leandro Minku, Huiyu Zhou
Concept drift is a major problem in online learning due to its impact on the predictive performance of data stream mining systems. Recent studies have started exploring data stream…
Cross-Modality Clustering-based Self-Labeling for Multimodal Data Classification
Paweł Zyblewski, Leandro L. Minku
Technological advances facilitate the ability to acquire multimodal data, posing a challenge for recognition systems while also providing an opportunity to use the heterogeneous na…
SMOClust: Synthetic Minority Oversampling based on Stream Clustering for Evolving Data Streams
Chun Wai Chiu, Leandro L. Minku
Many real-world data stream applications not only suffer from concept drift but also class imbalance. Yet, very few existing studies investigated this joint challenge. Data difficu…
Tackling Virtual and Real Concept Drifts: An Adaptive Gaussian Mixture Model
Gustavo Oliveira, Leandro Minku, Adriano Oliveira
Real-world applications have been dealing with large amounts of data that arrive over time and generally present changes in their underlying joint probability distribution, i.e., c…
Multi-Source Transfer Learning for Non-Stationary Environments
Honghui Du, Leandro L. Minku, Huiyu Zhou
In data stream mining, predictive models typically suffer drops in predictive performance due to concept drift. As enough data representing the new concept must be collected for th…
Proceedings of the IJCAI 2017 Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17)
Shuo Wang, Leandro L. Minku, Nitesh Chawla +1
With the wide application of machine learning algorithms to the real world, class imbalance and concept drift have become crucial learning issues. Class imbalance happens when the…
A Systematic Study of Online Class Imbalance Learning with Concept Drift
Shuo Wang, Leandro L. Minku, Xin Yao
As an emerging research topic, online class imbalance learning often combines the challenges of both class imbalance and concept drift. It deals with data streams having very skewe…